There’s much to discover regarding how CBD affects you. CBD however, early research suggests promising results, according to Seema Bonney, M.D. Director and founder of the Anti-Aging & Longevity Center of Philadelphia.
“Cannabidiol–commonly referred to as CBD–is emerging as a novel, non-addictive way to help with pain, both orally and topically,” says Bonney.
The whole process begins with the endocannabinoid system , or ECS. The discovery of the ECS in the 90s. the cell-level communication network is also referred to as the “master regulator system” due to its importance in ensuring homeostasis.
“The Endocannabinoid System is involved in regulating many functions of the body, including appetite, mood, pain perception and metabolic rate,” the doctor explains. It also plays a part in the way we sleep.
Although our understanding of how this functions is “still changing,” per Bonney, we do know some basic concepts. The ECS comprises two major components: cannabinoids as well as cannabinoid receptors (CB1 and CB2).
Our bodies produce their individual cannabinoids (i.e. the endocannabinoids) from specific organs and tissues. However they can also be consumed from plants that are essential to. They are referred to as phytocannabinoids.
The process of binding to receptors is different depending on the cannabinoid kind, however, CBD isn’t thought to actually bind to receptors. However, certain phytocannabinoids, such as CBD are believed to act in a way to control the ECS.
For topically applied CBD products it is necessary to work with the ECS to aid in returning our bodies back to a state of equilibrium, which could be an effective method to manage inflammation and pain around or just below the skin’s surface as suggested by Bonney.
“From research on animals we have discovered that CBD produces analgesic effects by regulating the endocannabinoid system, pain sensing and inflammatory systems.” Bonney explains.
For instance, a study revealed the hemp-derived CBD cream has been proven to decrease joint swelling and pain in the rodent model of mice1 (though further research is needed to confirm its effects in humans).
Another benefit of CBD cream is that it permits you to concentrate on specific body parts unlike other CBD forms, like capsules or tinctures which work in a systemic manner. Topical products such as CBD creams, lotions or balms are ideal for treating areas which hurt, like back discomfort, according to holistic physician Sony Sherpa M.D..
Take note that the hemp-based topically applied CBD will not penetrate as deep into the skin therefore it doesn’t affect other body parts like sleep, mood, etc.
Read the entire story on SportsLook Notebook for Japan Sports.[JAPAN SPORTS NOTEBOOK] Hideki Matsuyama Leaves Tourney due to neck pain
In the middle of March 2022, Hideki Matsuyama was unable to play due to the back injury. He was forced out of a tournament in the second round of the build-up to the Masters event, which took place just a few weeks after.
However, Matsuyama who was who was the 2021 Masters champion He was back playing golf just in time to play in last year’s event at Augusta, Georgia. He was tied for 14th place.
The 2019 Masters will be played from between April 7 and 10 at Augusta National Golf Club, and Matsuyama has been ruled out of the World Golf Championships (WGC)-Dell Technologies Match Play tournament in Austin, Texas, on Thursday, March 24, due to a neck strain.
Similar season, different illness.
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“I felt a bit stiff in my neck this morning as I was getting ready on the range. It prevented me from taking an entire shot,” Matsuyama said in an announcement.
“As as a precautionary measure I decided to forfeit the match Max [Homa] in order to rest before advancing to play in the Valero Texas Open in the coming week (March 30 – April 2.).”
Hideki Matsuyama chips on the fifth hole in the first round at the WGC-Dell Technologies Match Play tournament. (Eric Gay/AP)
On the 12th of March, Matsuyama finished fifth in The Players Championship in Ponte Vedra, Florida, carding 9-under 279. He missed the cut in each of his previous tournaments which included The Genesis Invitational and Arnold Palmer Invitational in March and February.
Keep reading for the complete story, which includes information on boxing soccer, auto racing basketball, baseball, and sumo via SportsLook.
Autor: Ed Odeven
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Discover Ed in JAPAN Forward’s official web site SportsLook. Keep track of his JAPAN Sports Notebook on Sundays, [Odds ] and Evenson weekdays and on Twitter @ed_odeven.
Objective To evaluate the comparative effectiveness and safety of analgesic medicines for acute non-specific low back pain.
Design Systematic review and network meta-analysis.
Data sources Medline, PubMed, Embase, CINAHL, CENTRAL, ClinicalTrials.gov, clinicialtrialsregister.eu, and World Health Organization’s International Clinical Trials Registry Platform from database inception to 20 February 2022.
Eligibility criteria for study selection Randomised controlled trials of analgesic medicines (eg, non-steroidal anti-inflammatory drugs, paracetamol, opioids, anti-convulsant drugs, skeletal muscle relaxants, or corticosteroids) compared with another analgesic medicine, placebo, or no treatment. Adults (≥18 years) who reported acute non-specific low back pain (for less than six weeks).
Data extraction and synthesis Primary outcomes were low back pain intensity (0-100 scale) at end of treatment and safety (number of participants who reported any adverse event during treatment). Secondary outcomes were low back specific function, serious adverse events, and discontinuation from treatment. Two reviewers independently identified studies, extracted data, and assessed risk of bias. A random effects network meta-analysis was done and confidence was evaluated by the Confidence in Network Meta-Analysis method.
Results 98 randomised controlled trials (15 134 participants, 49% women) included 69 different medicines or combinations. Low or very low confidence was noted in evidence for reduced pain intensity after treatment with tolperisone (mean difference −26.1 (95% confidence intervals −34.0 to −18.2)), aceclofenac plus tizanidine (−26.1 (−38.5 to −13.6)), pregabalin (−24.7 (−34.6 to −14.7)), and 14 other medicines compared with placebo. Low or very low confidence was noted for no difference between the effects of several of these medicines. Increased adverse events had moderate to very low confidence with tramadol (risk ratio 2.6 (95% confidence interval 1.5 to 4.5)), paracetamol plus sustained release tramadol (2.4 (1.5 to 3.8)), baclofen (2.3 (1.5 to 3.4)), and paracetamol plus tramadol (2.1 (1.3 to 3.4)) compared with placebo. These medicines could increase the risk of adverse events compared with other medicines with moderate to low confidence. Moderate to low confidence was also noted for secondary outcomes and secondary analysis of medicine classes.
Conclusions The comparative effectiveness and safety of analgesic medicines for acute non-specific low back pain are uncertain. Until higher quality randomised controlled trials of head-to-head comparisons are published, clinicians and patients are recommended to take a cautious approach to manage acute non-specific low back pain with analgesic medicines.
Acute low back pain (for less than six weeks’ duration) is a common presentation in primary care.1 Acute non-specific low back pain, in which a pathoanatomical cause of pain cannot be reliably determined, represents more than 90% of these presentations.2 Clinical practice guidelines recommend advice, reassurance, encouragement of physical activity, and self-management of symptoms as first line care.3 Second line care includes non-pharmacological interventions (eg, manual therapy) and analgesic medicines.3456 Surveys about primary care indicate many adults receive an analgesic medicine (48% in the UK and 61% in Australia).78
Clinicians who prescribe medicines for low back pain must choose between medicines with different analgesic properties and safety profiles. Systematic reviews that compared medicines with placebo only partially inform this decision.91011121314151617 A network meta-analysis combines direct and indirect information across a network of randomised clinical trials to estimate the comparative effectiveness of multiple treatments.18 This study type incorporates evidence from placebo controlled trials and trials of comparative effectiveness.19 A previous network meta-analysis compared the effectiveness of classes of analgesic medicines as part of a broader evaluation of pharmacological and non-pharmacological interventions.20 However, no comprehensive evaluation of individual medicines is available to inform clinical decision making for the best medicine for acute non-specific low back pain.2122
Our study used a network meta-analysis to evaluate the comparative effectiveness of analgesic medicines for adults with acute non-specific low back pain.
Methods
We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses-network meta-analysis (PRISMA-NMA) statement for this article.23 This report is part of a larger project (PROSPERO CRD42019145257) evaluating analgesic medicines for low back pain. The published protocol appears in supplement 1,24 and protocol updates are in supplement 2a and 2b.
Eligibility criteria
We included randomised controlled trials of adults (≥18 years) with acute non-specific low back pain.1 We included randomised controlled trials that compared an analgesic medicine with another analgesic medicine, placebo medicine, or no treatment (including continuation of usual care or being placed on a waitlist). We did not restrict our criteria by language or publication status. We excluded randomised controlled trials with enriched enrolment because this method violates the transitivity assumption.242526
We included non-steroidal anti-inflammatory drugs, paracetamol, opioids, anticonvulsants, antidepressants, skeletal muscle relaxants, or corticosteroids from the World Health Organization Anatomical Therapeutic Chemical system (supplement 2c).27 Medicines must have had a license for use in humans in 2021 by the US Food and Drug Administration,28 UK Medicine and Healthcare Products Regulatory Agency,29 European Medicines Agency,30 or Australian Therapeutic Goods Administration.31 We included additional licensed medicines in these classes that were identified during the review process. Medicines must have been administered systemically (eg, oral, intravenous, and intramuscular) as a single drug or combination formulations, at any dose. We excluded non-systemic administrations (eg, topical and epidural). Trials that used non-pharmacological co-interventions were included and were considered in the assessment of transitivity.24
We only included trials that assessed the effects of medicines that had been administered for a minimum of 24 h or, where single administration was used, outcomes at the end of treatment had to have been measured a minimum of 24 h later. This threshold excluded trials that tested the analgesic effect of medicines on immediate term outcomes only, which typically examined acute emergency care or experimental settings and is different to primary care.3233
Data sources
We searched five electronic databases and three clinical trial registers (Medline, PubMed, Embase, CINAHL, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, EU Clinical Trials Register, and the World Health Organization’s International Clinical Trial Registry Platform) from database inception until 20 February 2022. Full search strategies appear in supplement 2d. We also searched previous reviews and reference lists of included trials, which returned no additional records.
Study identification
Two authors (MAW and one of MDJ, MCF, AGC, RRNR, HBL, ADH, or SSh) independently screened records by title and abstract and full text in Covidence.34 Authors were experienced with similar eligibility criteria17353637 and were trained for this review. Discrepancies were resolved through discussion and arbitration from a third author (JHM). If required, the corresponding author of the trial was contacted up to three times to determine record eligibility. All included records underwent linkage to establish unique trials.38
Outcomes and data extraction
Two authors (MAW and one of MDJ, MCF, AGC, RRNR, HBL, ADH, or SSh) independently extracted data from included trials into standardised spreadsheets, with discrepancies resolved through discussion. Authors were experienced with these extraction sheets.17353637
We extracted information on trial characteristics (country, setting, number of trial sites, sample size, duration), participants (diagnosis, duration of low back pain, numbers of men and women, pain intensity at baseline, comorbidities), interventions (medicine, route of administration, duration of intervention, dosage, usage of rescue medication, provision of usual care, co-interventions prescribed by trial investigators), and outcomes.
The primary outcomes were low back pain intensity (0-100 scale, values as integers) at the end of treatment, and safety (number of participants who had any adverse event during the treatment period).39 The end of treatment endpoint accounts for the different treatment durations of medicines. Secondary outcomes were low back specific function (0-100 scale, values as integers), harm (number of participants who had a serious adverse event during the treatment period),3940 and acceptability (number of participants who stopped participation in the trial for any reason before the end of treatment).41
For pain intensity and function, data from continuous self-reported scales were extracted at the time point closest to end of treatment. The hierarchy for extraction of data formats was (1) group mean and standard deviation at end of treatment, (2) group mean change from baseline and standard deviation, and (3) between group differences. Data from studies reporting multiple measures for pain intensity were prioritised as follows: 100 mm visual analogue scale, 10 cm visual analogue scale, 11 point numerical rating scale, rating scale from a composite measure, and ordinal scale.1736 Data from studies that reported multiple measures for function were prioritised similarly: Oswestry Disability Index,42 Roland Morris Disability Questionnaire,43 rating scale from a composite measure, ordinal scale.1736 Data for pain intensity and function were normalised to 0-100 scales before analysis to improve clinical interpretability.91044 Data presented in other forms (eg, median or standard error) were transformed.4546 If measures of variance were not reported and unobtainable, the median standard deviation value from included studies with low risk of bias was imputed (30/100 for pain intensity and 35/100 for function). The number of participants per group who had one or more events was extracted for safety, harm, and acceptability.
The corresponding author of a trial was contacted up to three times via email to request missing outcomes (eg, mean and standard deviation for pain intensity or function and number of participants who had adverse events) and demographic data (eg, age, sex, baseline pain intensity).
Risk of bias
Two authors (MAW and one of MDJ, MCF, AGC, RRNR, HBL, ADH, or SSh) independently appraised outcome level risk of bias using the Cochrane tool for assessing risk of bias in randomised trials (RoB 2).47 For each outcome, we assessed risk of bias across five domains: randomisation process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. We visualised risk of bias ratings using the robvis tool.48
Data synthesis and analysis
Evaluation of transitivity
Transitivity, the key assumption for valid estimation of indirect comparisons, was assessed before conducting analyses.184950 The distributions of prespecified effect modifiers were examined across network comparisons: baseline pain intensity (continuous), presence of co-interventions (binary), sample size (continuous),51 whether participants were required to be previously untreated to the test medicine (binary), and medicine dose (binary).24 Dose was classified as within or above the standard dosing range, sourced from the Prescriber’s Digital Reference,52 Monthly Index of Medical Specialties,53 or Australian Medicines Handbook.54 If unavailable, the licensed dosing range was used.
Measures of effect
We analysed comparisons of between group level mean and standard deviation values for pain intensity and function at end of treatment using mean difference with 95% confidence intervals on a 0-100 scale (values as integers). We also analysed comparisons of between group level event rates for safety, harm, and acceptability by risk ratio with 95% confidence intervals. Effects were considered statistically significant when the 95% confidence interval did not cross the null. For pain intensity and function, between group differences were considered small if 5-10 points, moderate if more than 10-20 points, and large if more than 20 points.5556 Confidence in the effect estimates was judged using Confidence in Network Meta-Analysis (CINeMA),5758 which considered six domains: trial level risk of bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence. Descriptions of how we considered each domain are available in our protocol.24
Analytical approach
We performed a random effects network meta-analysis using the netmeta package in R, which implements a frequentist method based on a graph theoretical approach, according to electrical network theory.5960 The method follows a two stage approach, in which study effect estimates and their variances are synthesised and weighted by the inverse of their variance. We assumed a common heterogeneity variance across the network for each outcome, which was added to each comparison of the network and estimated via the generalised DerSimonian-Laird method of moments estimator.6162 Dependent observations from trials with more than three groups were accounted for with a back calculation of variances.59 Results from the network meta-analysis were presented as summary relative effect sizes (mean difference or risk ratio) along with 95% confidence intervals, derived assuming a normal distribution of the effects, for each possible pair of treatments. We calculated P scores (the frequentist equivalent of the surface under the cumulative ranking curve (SUCRA)) to measure the extent of certainty that a treatment is better than any other treatment.63 Estimates of heterogeneity and the proportion of variability that was not due to sampling error were calculated for each comparison. Statistics were also calculated for heterogeneity across the network, within designs, and between designs. We evaluated coherence (statistical agreement between direct and indirect treatment effects in closed loops)19 by use of these heterogeneity statistics, and complemented with the design by treatment interaction model,6465 the net heat plot,66 and the Separating Indirect from Direct Evidence (node splitting) approach.67 Small trial effects were evaluated using comparison adjusted funnel plots, with reference to placebo (supplement 3).68
Node definitions
The nodes for the primary analysis of each outcome were defined at the level of the medicines. Each single drug or combination formulation was a separate node. We considered licensed sustained release formulations as separate nodes to conventional formulations of the same medicine. Different routes of administration for the same medicine (or combination) were merged into the same node. Where trials reported more than one intervention group within the same dosing range, we combined the outcome data.46
The secondary analysis considered classes of medicines as separate nodes in the network for each outcome. Medicines were combined into classes based on expertise of the author team, clinical guidelines, and previous reviews91011121314151617 (supplement 2a).
Additional analyses
Prespecified sensitivity analyses of the primary outcomes (pain intensity and safety) assessed the effect of removing trials with overall high risk of bias, removing medicines with dosages above the standard or licensed dosing range, removing groups with baseline pain intensity above 70/100, removing trials with total sample sizes of fewer than 50 participants, and removing trials where data were imputed. These analyses were done where the network structure remained the same as the primary analysis. We also conducted a post hoc sensitivity analysis in which we removed two trials that were published in predatory journals, with concerns for research integrity (supplement 2b). We were asked during peer review to perform a post hoc sensitivity analysis on industry sponsorship.
Patient and public involvement
This study did not involve any patient representatives or members of the public in a formal capacity. As a result of limited funding, we were not able to engage with consumer groups and the review protocol was drafted before the involvement of patients and the public in reviews became standard practice. The review team provided the results of the review to their clinical colleagues and individuals from the general public with whom they had personal relationships. The team sought informal feedback from these individuals based on their experiences with low back pain as either patients or clinicians.
Results
We identified 154 eligible records corresponding to 124 eligible trials. Twenty six trial registrations were noted as terminated, ongoing, or unknown. Therefore, we included 98 randomised controlled trials published between 1964 and 2021 (fig 1). The 1300 records excluded during full text screening are provided in supplement 2e. The 98 included trials (n=15 134 participants) evaluated 70 unique interventions (69 medicines or combinations, and placebo; supplement 2f). No trials included a no treatment group.
Fig 1 Flow diagram of study identification, screening, and inclusion “>
Fig 1
Flow diagram of study identification, screening, and inclusion
Participant characteristics (table 1) reflected typical acute non-specific low back pain populations: 49% women, mean age mostly between 30 and 60 years, low back pain duration ranged from 24 h to 21 days, and median pain intensity at baseline of 65/100 (interquartile range 57-72) across included trials. Thirty eight (39%) of 98 trials were placebo controlled, 66 trials (67%) masked both participants and clinicians, and 40 trials (41%) reported industry sponsorship. Analyses on industry sponsorship are reported in supplement 2. Characteristics about participants, interventions, and outcomes are available in supplement 2g and 2h. Characteristics about trial registrations noted as terminated, ongoing, or unknown are available in supplement 2i.
Table 1
Summary of studies included in the review
View this table:
Forty two medicines were administered as a monotherapy and 27 as combinations (supplement 2f). Treatment duration ranged from one day (single administration) to 42 days. Eighty (82%) of 98 trials administered medicines orally, and 168 (98%) of 172 medicines were administered within a standard or licensed dosing range (table 1). Two trials6970 reported two or more intervention groups within the same dosing range that we combined.
Assessment of transitivity and incoherence
A comprehensive assessment of transitivity was limited by the small number of trials per comparison (supplement 2j). During the evaluation of network diagnostics, four trials were identified that had methodological discrepancies inconsistent with the network (two based on incoherence within the network and two based on heterogeneity within treatment comparisons) and were removed from all analyses (supplement 2k). We then re-evaluated the diagnostics for the updated models and agreed to proceed to interpreting treatment estimates. However, some comparisons show evidence of unexplained incoherence and, therefore, should be interpreted with caution. Important examples are the network meta-analysis effects for the outcome pain intensity for the comparisons ibuprofen versus placebo and paracetamol versus placebo, in which discrepancies between direct and indirect evidence resulted in a P<0.10 for the Separating Indirect from Direct Evidence approach. The full network diagnostics for the updated models are presented in supplement 3. Any remaining concerns about network heterogeneity and incoherence were addressed via downgrading confidence in estimates. A summary of confidence in effect estimates is provided in supplement 2l. Common reasons for downgrading confidence in estimates were imprecision, heterogeneity, and risk of bias. League tables with estimates and confidence for all comparisons are provided in the supplement and spreadsheets are available on the Open Science Framework.
Primary analysis: nodes as medicines
Pain intensity
Pain intensity was measured in all 98 trials. Three trials measured pain intensity only during movement. Pain intensity was measured with a 100 mm visual analogue scale (23 trials), a 10 cm visual analogue scale (16 trials), a 11 point numerical rating scale (seven trials), or another ordinal scale (24 trials). Data for pain intensity were analysed in 66 (67%) of 98 trials. Ten trials were at low risk of bias, 36 trials had some concerns, and 20 trials were at high risk of bias (supplement 2m). Endpoint data were reported in 50 trials and changes from baseline were reported in 16 trials. Fifteen trials (23%) required standard deviation imputation. Pain intensity data were transformed in 16 trials: 12 used count data, two used 95% confidence interval for group mean, one used median, one used range. The 66 trials did not form a connected network (fig 2, fig 3). The placebo network compared 39 interventions (38 medicines and the central node of placebo) in 54 trials (fig 2). Most comparisons consisted of a single trial, ranging from one to three, and had a limited number of closed loops. Direct evidence was available for 52 (7%) of 741 comparisons. The naproxen network compared 13 medicines in 10 trials (the central node was naproxen) with one trial per comparison (fig 3). Direct evidence was available for 14 (18%) of 78 comparisons. Two trialswere not included in either network because these trials do not connect to any part of the network).
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Fig 2
Network plot for pain intensity for medicines for placebo network. Within each network, the node size is proportional to the sample size of each intervention and the line thickness is proportional to the number of trials in the comparison (also indicated by the numbers). Light purple shading indicates trials with more than two arms. The two trials that did not connect to the network and that were not included were hydrocodone plus ibuprofen versus oxycodone plus paracetamol (Palangio 2002; for full details of references see supplement 2); and etodolac plus thiocolchicoside versus etodolac plus tolperisone (Garg 2019)
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Fig 3
Naproxen network plot for pain intensity for medicines. Within each network, the node size is proportional to the sample size of each intervention and the line thickness is proportional to the number of trials in the comparison (also indicated by the numbers). Light purple shading indicates trials with more than two arms
Data were of very low confidence in 648 (87%) of 741 comparisons and of low confidence in 93 (13%) of 741 of comparisons in the placebo network (supplement 2l). Tolperisone (mean difference −26.1 (95% confidence interval −34.0 to −18.2), low confidence), aceclofenac plus tizanidine (−26.1 (−38.5 to −13.6), very low confidence), and pregabalin (−24.7 (−34.6 to −14.7), low confidence) might be associated with the largest reductions in pain intensity compared with placebo (fig 4). Additionally, for statistically significant reductions, very low confidence was reported for large reductions (mean difference of >20 points) for four medicines, moderate reductions (>10-20 points) for seven medicines; and small reductions (5-10 points) for three medicines (fig 4). The estimates of comparative effectiveness and rankogram are in supplement 2n. No significant differences were noted between all medicines with large reductions in pain intensity compared with placebo, with data low or very low confidence. Similarly, low or very low confidence in evidence was reported for no significant differences between the medicines with large reductions in pain intensity and some medicines with moderate reduction in pain intensity compared with placebo. Some significant differences between medicines were noted; for example, low confidence data suggested that tolperisone is superior to carisoprodol at reducing pain intensity (mean difference −13.7 (−24.9 to −2.5)).
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Fig 4
Forest plot for analgesic medicines and pain intensity. Medicines are ordered according to their P score ranking and compared with placebo. Point estimates refer to the mean difference. The bars indicate 95% confidence interval. Direct comparisons refer to the number of included studies comparing the intervention to placebo. Random effects model: τ2=11.51. CI=confidence interval; SR=sustained release
Confidence could not be evaluated for the naproxen network because of the small number of trials. Six medicines might be associated with a statistically significant reduction in pain intensity compared with naproxen (supplement 2o). The estimates of comparative effectiveness and rankogram are in supplement 2p. Sensitivity and post hoc analyses for pain intensity with nodes as medicines are reported in supplement 2q.
Safety
Ninety two trials reported measuring safety, but only 68 trials (74%) were analysed for the number of participants who reported an adverse event. The primary reasons for data unavailability were reports of only numbers of adverse events, rather than number of participants, or no data for the subset of participants with acute non-specific low back pain. Nine trials were at low risk of bias, 41 trials had some concerns, and 18 trials were at high risk of bias (supplement 2r). One network compared 55 interventions (54 medicines and placebo) in 66 trials (fig 5), and two trials did not connect to the network. All comparisons in the network consisted of a one or two trials and the number of closed loops was small. Direct evidence was available for 70 (4.7%) of 1485 comparisons. Effect estimates were analysed as risk ratios.
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Fig 5
Network plot for safety for medicines. The node size in proportional the sample size of each intervention. The line thickness is proportional the number of trials in the comparison (also indicated by the numbers). Light blue shading indicates trials with more than two arms. The two trials that did not connect to the network and were not included were hydrocodone plus ibuprofen versus oxycodone plus paracetamol (Palangio 2002; for full details of references see supplement 2); and etodolac plus thiocolchicoside versus etodolac plus tolperisone (Garg 2019)
Comparisons were of very low confidence in 34 (2%) of 1485, low confidence in 1274 (86%) of 1485, moderate confidence in 168 (11%) of 1485, and high confidence in nine (1%) of 1485 (supplement 2l). Tramadol (risk ratio 2.6 (95% confidence interval 1.5 to 4.5), moderate confidence), paracetamol plus sustained release tramadol (2.4 (1.5 to 3.8), moderate confidence), baclofen (2.3 (1.5 to 3.4), low confidence), and paracetamol plus tramadol (2.1 (1.3 to 3.4), moderate confidence) might be associated with increased adverse events during treatment compared with placebo (fig 6). The estimates of comparative effectiveness and rankogram are provided in supplement 2s. Data had high to very low confidence that these four medicines were also more likely to increase adverse events compared with other medicines. For example, moderate confidence data suggested that tolperisone was associated with fewer adverse events than tramadol (0.2 (0.1 to 0.7)) and high confidence data suggested that paracetamol was associated with fewer adverse events than paracetamol plus sustained release tramadol (0.4 (0.2 to 0.6)).
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Fig 6
Forest plot for analgesic medicines with safety (any adverse event). Medicines are ordered according to their P score ranking and compared with placebo. Point estimates refer to the risk ratio. The bars indicate the 95% confidence interval. Direct comparisons refer to the number of included studies comparing the intervention to placebo. Random effects model: τ2=0.015.CI=confidence interval; SR=sustained release
The quality of adverse event measurement and reporting varied across trials. Generally, trials did not distinguish between an adverse event (an untoward medical occurrence) and an adverse effect (an untoward medical occurrence judged as related to treatment). Brief descriptions of adverse events reported in each trial are available in supplement 2h. Most commonly reported adverse events were related to the gastrointestinal system (nausea, dyspepsia, vomiting, diarrhoea) and the nervous system (drowsiness, dizziness, headache). Sensitivity and post hoc analyses for safety with nodes as medicines are reported in supplement 2t.
Secondary analysis: medicine classes
The secondary analysis of medicine classes included 65 trials (n=1107 participants; 33 trials that only compared medicines within the same class, primarily non-selective non-steroidal anti-inflammatory drugs, were excluded). A list of the 22 interventions (21 different classes or combinations, and placebo) is available in supplement 2u.
Pain intensity
Pain intensity was analysed in 45 trials. One network compared 16 interventions (15 classes and placebo) in 44 trials, and one trial did not connect to the network (supplement 2v). Direct evidence was available for 22 (18%) of 120 comparisons. Of the 120 comparisons, evidence was of very low confidence in 114 (95%), low confidence in five (4%), and moderate confidence in one (1%) (supplement 2l).
Anticonvulsants (mean difference −18.6 (95% confidence interval −30.1 to −7.1), very low confidence), non-benzodiazepine antispasmodic (−14.3 (−18.8 to −9.7), very low confidence), non-selective non-steroidal anti-inflammatory drugs plus non-benzodiazepine antispasmodic (−12.7 (−17.9 to −7.5), very low confidence), non-selective non-steroidal anti-inflammatory drugs plus strong opioids plus paracetamol (−13.1 (−25.0 to −1.1), low confidence), non-selective non-steroidal anti-inflammatory drugs plus antispastic (−13.1 (−25.5 to −0.7), low confidence), non-selective non-steroidal anti-inflammatory drugs plus anticonvulsants (−12.3 (−23.3 to −1.3), very low confidence) might be associated with the moderate reductions in pain intensity compared with placebo (fig 7). The estimates of comparative effectiveness and rankogram are in supplement 2w. Very low confidence was shown for no statistically significant differences between any of the medicine classes that reduced pain intensity compared with placebo. Some differences between classes were noted; for example, evidence showed very low confidence that anticonvulsants were superior to weak opioids for reducing pain intensity (−14.5 (−28.7 to −0.4)). Sensitivity and post hoc analyses for pain intensity with nodes as medicine classes are reported in supplement 2x.
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Fig 7
Forest plot for analgesic medicine classes with pain intensity. Medicine classes are ordered according to their P score ranking and compared with placebo. Point estimates refer to the mean difference. The bars indicate 95% confidence interval. Direct comparisons refer to the number of included studies comparing the intervention to placebo. Random effects model: τ2=23.93. CI=confidence interval; COX-2=cyclooxygenase 2; NSAIDs=non-steroidal anti-inflammatory drugs
Safety
Safety was analysed in 46 trials. One network compared 19 interventions (18 classes and placebo) in 45 trials, and one trial did not connect to the network (supplement 2y). Direct evidence was available for 27 (16%) of 171 comparisons. Of 171 comparisons, seven (4%) were of very low confidence, 109 (64%) were of low confidence, 50 (29%) were of moderate confidence, and five (3%) were of high confidence (supplement 2l).
Compared with placebo, increased adverse events during treatment might be associated with antispastic drugs (risk ratio 2.3 (95% confidence interval 1.4 to 3.8), low confidence), weak opioids (1.9 (1.3 to 2.9), moderate confidence), non-selective non-steroidal anti-inflammatory drugs plus strong opioids plus paracetamol (1.9 (1.1 to 3.2), high confidence), weak opioids plus paracetamol (1.9 (1.3 to 2.7), moderate confidence), and non-selective non-steroidal anti-inflammatory drugs plus non-benzodiazepine antispasmodic (1.5 (1.1 to 2.1), moderate confidence) (supplement 2z). The estimates of comparative effectiveness and the rankogram are in supplement 2aa. Findings were of high to very low confidence that these classes were also more likely to increase adverse events compared with the other classes. For example, non-selective non-steroidal anti-inflammatory drugs plus strong opioids plus paracetamol were of high confidence and was associated with more adverse events than paracetamol (2.2 (1.2 to 4.4)). Sensitivity and post hoc analyses for safety with nodes as medicine classes are reported in supplement 2ab.
Secondary outcomes
We also analysed secondary outcomes with nodes as medicines and nodes as medicine classes. We did not perform sensitivity and post hoc analyses for the secondary outcomes. Results for function are reported in supplement 2ac (nodes as medicines) and supplement 2ad (nodes as medicine classes). Results for acceptability are reported in supplement 2ae (nodes as medicines) and supplement 2af (nodes as medicine classes). Results for harm are reported in supplement 2ag.
Discussion
Our review of analgesic medicines for acute non-specific low back pain found considerable uncertainty around effects for pain intensity and safety. The findings were of low or very low confidence that several medicines might be associated with large reductions in pain intensity compared with placebo, and some medicines might be more effective than other medicines. Several other medicines might be associated with an increased risk of adverse events compared with placebo, as well as compared with other medicines. In the secondary analysis of medicine classes, low or very low confidence evidence showed that seven classes might be associated with small to moderate reductions in pain intensity compared with placebo, with no statistically significant differences between these classes. However, low confidence showed that two of these classes increased the risk of adverse events compared with placebo.
Implications for clinicians and policy makers
Judgements of low or very low confidence in this review warrant caution for the clinical interpretation of these effects, which might change markedly with future research. Most effects were derived solely from indirect evidence and the findings were not robust to sensitivity analyses, with many effects becoming non-significant after the removal of trials based on different methodological considerations (eg, risk of bias). Similar findings of moderate to large effects for pain intensity but low confidence have been reported for several non-pharmacological interventions used for acute non-specific low back pain: superficial heat, massage, manual therapy, and acupuncture.2 Similar levels of uncertainty were identified in a network meta-analysis published in 2022 of 46 randomised controlled trials that compared pharmacological and non-pharmacological interventions for acute and subacute low back pain.20
Clinical practice guidelines recommend non-pharmacological treatments in first line and second line care for acute non-specific low back pain.3 Given the favourable natural history for most patients,71 we believe that clinicians and patients should take a cautious approach to the use of analgesic medicines. Similarly, policy makers should recommend a cautious approach when considering analgesic medicines, prioritising the minimisation of harm. Another consideration for clinicians and guideline developers is the legal availability of medicines. We included medicines licensed across the UK, Australia, USA, and Europe, which might not include medicines licensed in other countries and does not imply that the same medicines are available everywhere. Our estimates of comparative effectiveness suggest no differences between several medicines that were superior to placebo, meaning clinicians can incorporate our findings, a medicine’s availability, clinical expertise, and patient preferences when choosing an analgesic medicine.
Strengths and limitations
We believe that this review is the most comprehensive in the field. We preregistered and published the protocol and made our updates transparent. Our comprehensive search included published and unpublished literature in any language. Our rigorous method ensured as much data as possible were included, with scrutiny by an expert team. We closely examined network diagnostics to explore network heterogeneity, inconsistency, and incoherence (steps that are not often adequately undertaken)72 and we attempted to resolve these issues when they arose. However, this study has limitations. Firstly, we aspired to select a sample reflective of acute non-specific low back pain, but patients might differ across clinical settings.32 Secondly, most included studies had concerns related to risk of bias. Thirdly, data were missing and imputation was required for continuous outcomes, despite attempts to contact authors. Fourthly, no network meta-analysis methods can account for the uncertainty of variance estimates (analogous to the Hartung-Knapp approach for pairwise meta-analysis) and we were unable to thoroughly explore the influence of potential effect modifiers (eg, treatment duration, route of administration) because of the limited data and poor network structure. Finally, adverse event data in some trials were reported in a way that made them unable to be included in this study. In future trials, we encourage investigators to report the number of participants who had any adverse event, as well as type and severity of those adverse events.
Future research
The evidence base includes many different analgesic medicines or combinations, mostly compared to placebo. Relatively few randomised controlled trials evaluate comparative effectiveness. The structure of this information is not yet optimal to inform clinical decision making and the potential for network meta-analysis to contribute improved estimates of effects was under-realised. Most estimates were derived solely from indirect evidence, a key contributor to the low or very low confidence. Confidence was not substantially improved in the secondary analysis.
Other aspects of trial conduct might be improved in future work. Key limitations were moderate to high risk of bias and missing data, which have established influences on effect estimates.51 Analgesic medicines with larger effect sizes came from trials with lower methodological quality. Similarly, wide confidence intervals often arose from smaller studies. This uncertainty is propagated when networks make many comparisons via indirect evidence only. Concerns exist about research integrity and the large decrease in pain intensity from pregabalin was no longer apparent in sensitivity analyses. Synthesis of the trials at low risk of bias was not possible with conventional methods for network meta-analysis because they did not form a connected network. Our review, together with established methods for future trial design,7374 might be an important guiding contribution to further research. We identified 10 ongoing trials that could contribute additional data to future updates of this study, described briefly in supplement 2i. No further reviews are needed until high quality randomised controlled trials are published.
Conclusion
Despite nearly 60 years of research involving more than 15 000 patients, high quality evidence to guide clinical decisions on analgesic medicines for acute non-specific low back pain remains limited. Similarly, evidence from the secondary analysis of medicine classes had low confidence. Clinicians and patients are advised to take a cautious approach to the use of analgesic medicines. No further reviews are needed until high quality studies are published.
What is already known on this topic?
Analgesic medicines are a common treatment for acute non-specific low back pain
Previous reviews have evaluated analgesic medicines compared with placebo, but the evidence for the comparative effectiveness of these medicines is limited
What this study adds
Low or very low confidence evidence suggests that some analgesic medicines might be superior for reducing pain intensity, limited by trial risk of bias and imprecision in effect estimates
Evidence of moderate to very low confidence suggests that some analgesic medicines might increase the risk of adverse events during treatment
Clinicians and patients are recommended to take a cautious approach to managing acute non-specific low back pain with analgesic medicines until higher quality trials of head-to-head comparisons are published
Every winter millions of Canadians travel to the Caribbean seeking sun, clean beaches and crystal-clear water.
This year, , travelers may have noticed something unpleasant waiting for their guests on the beach: smelly brown Sargassum.
In the last decade the smelly seaweed has been more prevalent on beaches throughout the Caribbean and in the south Atlantic Ocean. What’s happening? To comprehend, you must understand the concept of sargassum.
What is Sargassum?
Sargassum is a kind of seaweed that is brown (and an algae type) located within the Atlantic Ocean. It’s comprised of leafy parts and oxygen-rich round berry-like fragments that allow it to float in the water. It does not have seeds or roots.
Contrary to other varieties of seaweeds, Sargassum spends all of its life in the ocean, in small areas. Sargassum is typically located in the region known as Sargasso Sea. Sargasso Sea, and is known to move within a vortex, also known as the gyre. It is located in five million square kilometers of a belt which extends starting from Chesapeake Bay in the mid-Atlantic and all the way towards the Caribbean.
However, sargassum may form a raft, forming the appearance of a raft or patch. It follows a seasonal pattern that begins in spring, gaining its apex in summer, and then dying in the autumn.
Sargassum is a brown, leafy algae species which lives and reproduces on the ocean’s surface. Atlantic Ocean. (Alexandre Meneghini/Reuters)
Sometimes, large amounts of debris can be found on beaches, which could be a nuisance for beach-goers. However, they’re essential to some marine life, as they provide breeding grounds and food for animals like sea turtles, fish and many more.
“[Sargassumhas created massive floating masses that are floating in the sea’s open, which are fantastic, actually … due to the fact that they absorb carbon dioxide and also take in nutrients, they support a wide range of life and many species depend upon them.” stated Brigitta in Tussenbroek who is a scientist in the National Autonomous Universities in Mexico’s Institute of Marine Sciences and Limnology.
“Even even the American eel, also known as the Northern European eel, they would not be there in the absence of the Sargasso Sea, for instance. This is why it’s such a great system.”
Is it growing?
Although most sargassum stays within this gyre, it could travel on an unidirectional conveyor belt between areas like the Atlantic as well as the Caribbean. Scientists have observed a dramatic increase in the amount of sargassum that is washing off beaches across the Caribbean since the year 2011.
“Sometimes certain Gyres … slow down and some Sargassum flew away, and then headed to Cuba and then to Hispaniola and others ended in Mexico travel to in the Gulf of Mexico. Then, it returned back towards in the Sargasso Sea.” van Tussenbroek said.
“Since the year 2010-11 the sargassum began to build up in a different areathat is located to the north of the Equator, in the tropical Atlantic.”
The park guard Roberto Varela walks over sargassum seaweed, which has been piled up along the beach in Guanahacabibes peninsula, Cuba in June 2022. (Alexandre Meneghini/Reuters)
What is the reason for this? Scientists aren’t certain.
There are a variety of factors that could be involved, such as climate change and human activities, said van Tussenbroek.
Chuanmin Hu, a professor at the University of South Florida who examines these blooms using the Sargassum Watch System (SaWS) He said that a significant amount of the nutrients are derived from Saharan Desert dust that blows across the Atlantic Ocean.
However, there is also upwelling in the ocean according to him, in which water from the depths below the ocean surface is brought to the surface and, with it the addition of nutrients, which in turn further aids the blooms. Scientists believe that due to a change in climate, there might increase the rate of ocean upwelling. In addition, nutrients flow to the Atlantic from the Congo River in Africa and the Amazon River in South America.
“Now it is a matter of what is the dominant factor?” Hu declared. “We aren’t sure. It’s hard to measure their impact.”
What’s the state of affairs this year?
According to SaWS the month of January was the second consecutive month that sargassum increased by a third the previous time in the year 2018. Although it decreased to a low in the month of February expectation is it’ll grow and spread through the waters of Caribbean nations. This year was the year with the highest number of Sargassum recorded.
So far, the year has been a great one, Hu said, adding that during the month of December SaWS forecast that the year 2023 would be an additional record-setting year but there’s no assurance.
“All We can do is that this will be another year of major significance.”
How much do you think it will cost?
The month of January saw was more than 8 million metric tons of sargassum floating in the Atlantic Ocean, Hu said. In February, the number decreased to six-to-seven million tonnes. However, Hu claimed there might be more in March.
“Right now , they’re scattered both here and there, with a small amount. Within the belt, there’s a density below 0.1 percent,” Hu said. “But If you could place all of the sargassum in one location, what would be the area? It’s around, I believe 3000 square kilometers, without a gap.”
Aerial image of algae Sargassum in Cancun August. 13 2015. (Edgard Garrido/Reuters)
What are the consequences of big blooms?
The large-scale blooms scientists have observed forming since 2011 pose a challenge. Hotels and towns must bear the costs removal of these blooms so that they can protect an industry that is booming in tourism.
Also, there’s the worry about ecosystems.
Workers remove Sargassum algae from Punta Piedra Beach in Tulum located in the Mexican state of Quintana Roo on August. 11 2018, 2018. (Israel Leal/Reuters)
A large number of sargassum harm fragile shoreline ecosystems and can also choke coral reefs, decreasing their roughness and cover that makes them more vulnerable to the waves and offer less protection against hurricanes. They can also hinder sea turtles that are hatching from getting to the ocean.
The positive side is that there are certain uses for the vast quantities of sargassum. These include making it biofuel, or even for building materials.
Do I need to be worried?
If you’re headed for the beaches and you come across the masses of sand, there’s nothing to worry about, however the smell that comes from them as they break down could be unpleasant. It could also ruin the offshore snorkelling or swimming experience you may want to take part in.
“What I’d like to say now to visitors is to not be afraid,” Hu said. “This is an unnatural plant and it’s not harmful. If you consume excessive amounts, and [it is decomposed, it might be hazardous, but it’s poisonous generally speaking,” Hu said. ”
“And often even when they do accumulate on beaches, locals usually remove them. Therefore, if you’re planning plans to travel, go to Florida and visit the Caribbean. I’d rather not stress about things.”
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A healthy, active and athletic triathlete handed an extremely serious diagnosis after the athlete began suffering from painless headaches.
Kieran Shingler first noticed the headaches at Bonfire Night last year, the Liverpool Echo reports The 23-year-old native of Warrington tried Sudafed and paracetamol, but the symptoms persisted.
After a mere week and a half doctors advised him to go to A&E since the headaches have gotten more severe which made him feel exhausted and nauseated. He was tested and diagnosed with a high-grade cancerous brain tumor.
Read More:Mum urges parents to ‘trust in their intuitions after a ‘happy little boy’ was misdiagnosed, then taken to hospital for a ventilator
Kieran has had four major surgery on his brain and a traumatic surgery at Christmas Day. He’s currently on the benefit of a break of four weeks. He will begin his second round of chemotherapy in the beginning of April. It will be in tablet form. He will be able to use at his own home.
The girlfriend of 9 years Abbie Henstock, who is 24, stated that Kieran was always an active young man and has played football for all the time she’s been around Kieran for. But, since the diagnosis, he’s not in a position to participate in the sports which he is passionate about.
She added: “In 2020 lockdown he was really into triathlons. He purchased his first road bike in the 2020 lockdown and the rest is history very well.
“Kieran has joined the triathlon group, TriCentral UK, at the beginning of 2022. He took part in numerous triathlons and even the 70.3 in Chester in the summer of last year. Since the diagnosis Kieran hasn’t been able swim, go for an exercise, or even ride his bicycle.
“This is clearly a source of frustration and devastating for him since it has led to the loss of a significant amount of his fitness, but changing that to an advantage, the strength gained from it has been extremely beneficial to his health as both nurses and doctors have repeatedly told us.
Kieran as well as Abbie(Image: Abbie Henstock/Liverpool Echo)
“Even following his brain surgery for the first time the patient would still want to go for a walk to a local cafe. While undergoing treatment, he was walking with me too. We were amazed by how much he wanted to be out even if it was walking around the block. All the while the patient was undergoing intensive treatment. He had also undergone four major brain operations.”
Abbie has described her friend as “gentle” and laidback” and also said that his family members are extremely happy for him.
She stated: “Kieran is the most kind person. He is relaxed and doesn’t worry about everything. He is always smiling at his lips and will do his best to please everyone.
“He loves being with his family, as we’re very close as a family. He is an avid supporter of Liverpool FC and enjoys the company of our family and friends whether it’s going on a camping trip, dinner out, or a trip for a workout.
Kieran participating at an event he calls triathalons(Image: Abbie Henstock/Liverpool Echo)
“We are so incredibly satisfied with him. There aren’t enough words to express it. Our worlds turned upside down, and it’s been the most difficult couple of months in our lives, but we must accept the situation and live each day in the way it is.
“Myself and his mom dad, sister, and her boyfriend, as along with his extended family and friends are there with him all the way.”
Abbie Kieran’s friends and family members are currently looking to raise money to help various brain tumour charities. They’ve set up an account on GoFundMe and are participating in various events to raise funds.
The 24-year-old told the BBC: “Before Kieran got diagnosed we didn’t know much about brain tumors. I’d seen the latest reports about Tom Parker from The Wanted but didn’t pay much attention to the funding because I thought that it was treated in the same way as breast cancer, etc.
Abbie, Kieran and his mom, Lisa after he finished his first six-week cycle of chemotherapy and radiotherapy(Image from Abbie Henstock/Leeds Echo). Henstock)
“However we know now the extent of underfunding it. Every every year, in UK there are 2,374 teenagers aged between 16 and 24 have cancer diagnoses. In excess of PS700m is devoted to cancer research but only three percent of it is devoted to brain cancer.
“In the month of June, 110 membersof our family and friends will be taking part in the Warrington 5K, which is pretty dirty as part of the “Kieran’s Krew” and all the money we raise is going to Brain Cancer Research UK. We’re currently only PS8.5k.
“Myself Kieran’s sister, his mom, and his friend from his mum’s side will be taking a skydive to raise funds in support of Clatterbridge Cancer Care Centre. The money raised will go to the Young Adults’ ward, and the remaining 50% will go directly to the area that will need the most.
“Kieran’s triathlon club is participating in a 100 mile bicycle ride in April to raise money for Kieran. His former football team are going up Snowdon to raise funds to fund our GoFundMe page. A friend of his will be taking part in his his first ever triathlon on May. There’s too much happening that we’re overwhelmed and want to help everyone to get involved in raising money for brain Tumour charity.
“It’s Brain Tumour Awareness Month which is March. I consider is the ideal opportunity to tell our story. There was also a debate in the House of Commons on the March 9 concerning the funding of Brain Tumours.”
To donate to GoFundMe, click here Click here.
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