Quality of meta-analyses of non-opioid, pharmacological, perioperative interventions for chronic postsurgical pain: a systematic review
Bibliographic record
Abstract
BACKGROUND: In an attempt to aggregate observations from clinical trials, several meta-analyses have been published examining the effectiveness of systemic, non-opioid, pharmacological interventions to reduce the incidence of chronic postsurgical pain. OBJECTIVE: To inform the design and reporting of future studies, the purpose of our study was to examine the quality of these meta-analyses. EVIDENCE REVIEW: We conducted an electronic literature search in Embase, MEDLINE, and the Cochrane Database of Systematic Reviews. Published meta-analyses, from the years 2010 to 2020, examining the effect of perioperative, systemic, non-opioid pharmacological treatments on the incidence of chronic postsurgical pain in adult patients were identified. Data extraction focused on methodological details. Meta-analysis quality was assessed using the A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) critical appraisal tool. FINDINGS: Our search yielded 17 published studies conducting 58 meta-analyses for gabapentinoids (gabapentin and pregabalin), ketamine, lidocaine, non-steroidal anti-inflammatory drugs, and mexiletine. According to AMSTAR 2, 88.2% of studies (or 15/17) were low or critically low in quality. The most common critical element missing was an analysis of publication bias. Trends indicated an improvement in quality over time and association with journal impact factor. CONCLUSIONS: With few individual trials adequately powered to detect treatment effects, meta-analyses play a crucial role in informing the perioperative management of chronic postsurgical pain. In light of this inherent value and despite a number of attempts, high-quality meta-analyses are still needed. PROSPERO REGISTRATION NUMBER: CRD42021230941.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".