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Record W4353031842 · doi:10.1136/bmj-2022-072962

Comparative effectiveness and safety of analgesic medicines for adults with acute non-specific low back pain: systematic review and network meta-analysis

2023· review· en· W4353031842 on OpenAlexaff
Michael A. Wewege, Matthew K. Bagg, Matthew D. Jones, Michael C Ferraro, Aidan G Cashin, Rodrigo R N Rizzo, Amanda D. Hagstrom, Saurab Sharma, Andrew J. McLachlan, Christopher G. Maher, Richard O. Day, Benedict M. Wand, Neil E O’Connell, Adriani Nikolakopolou, Siobhan M. Schabrun, Sylvia M. Gustin, James H. McAuley

Bibliographic record

VenueBMJ · 2023
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsParkwood InstituteWestern University
FundersUniversity of New South Wales
KeywordsMedicineAnalgesicAdverse effectPregabalinPlaceboMeta-analysisConfidence intervalLow back painRandomized controlled trialRelative riskPhysical therapyAnesthesiaInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

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. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019145257.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.039
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.099
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0250.041
Bibliometrics0.0120.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.385
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations56
Published2023
Admission routes1
Has abstractyes

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