Anticonvulsants in the treatment of low back pain and lumbar radicular pain: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The use of anticonvulsants (e.g., gabapentin, pregabalin) to treat low back pain has increased substantially in recent years despite limited supporting evidence. We aimed to determine the efficacy and tolerability of anticonvulsants in the treatment of low back pain and lumbar radicular pain compared with placebo. METHODS: A search was conducted in 5 databases for studies comparing an anticonvulsant to placebo in patients with nonspecific low back pain, sciatica or neurogenic claudication of any duration. The outcomes were self-reported pain, disability and adverse events. Risk of bias was assessed using the Physiotherapy Evidence Database (PEDro) scale, and quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Data were pooled and treatment effects were quantified using mean differences for continuous and risk ratios for dichotomous outcomes. RESULTS: Nine trials compared topiramate, gabapentin or pregabalin to placebo in 859 unique participants. Fourteen of 15 comparisons found anticonvulsants were not effective to reduce pain or disability in low back pain or lumbar radicular pain; for example, there was high-quality evidence of no effect of gabapentinoids versus placebo on chronic low back pain in the short term (pooled mean difference [MD] -0.0, 95% confidence interval [CI] -0.8 to 0.7) or for lumbar radicular pain in the immediate term (pooled MD -0.1, 95% CI -0.7 to 0.5). The lack of efficacy is accompanied by increased risk of adverse events from use of gabapentinoids, for which the level of evidence is high. INTERPRETATION: There is moderate- to high-quality evidence that anticonvulsants are ineffective for treatment of low back pain or lumbar radicular pain. There is high-quality evidence that gabapentinoids have a higher risk for adverse events. PROTOCOL REGISTRATION: PROSPERO-CRD42016046363.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".