Perioperative use of pregabalin for acute pain—a systematic review and meta-analysis
Bibliographic record
Abstract
Evidence supporting postoperative pain management using pregabalin as an adjunct intervention across various surgical pain models is lacking. The objective of this systematic review was to evaluate "model-specific" comparative effectiveness and harms of pregabalin following a previously published systematic review protocol. MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched from inception through August 2013. Data were screened and single extraction with independent verification and dual risk of bias assessment was performed. Quality of evidence (QoE) was rated using the GRADE approach. Primary outcomes were pain relief at rest and on movement and reduction in postoperative analgesic consumption. A total of 1423 records were screened, and 43 studies were included. Perioperative pregabalin resulted in: 16% (95% confidence interval [CI], 9%-21%) reduction in analgesic consumption (moderate QoE, 24 trials) and a small reduction in the magnitude of pain in surgeries associated with pronociceptive pain. Per 1000 patients, 10 more will experience blurred vision (95% CI, 5-20 more; moderate QoE, 17 trials) and 41 more sedation (95% CI, 13-77 more, 17 trials). To prevent 1 case of perioperative nausea and vomiting, the number needed to treat is 11 (95% CI: 7-28, 25 trials). Inadequate evidence addressed outcomes of enhanced recovery and serious harms. Pregabalin analgesic effectiveness is largely restricted to surgical procedures associated with pronociceptive mechanisms. The clinical significance of observed pregabalin benefits must be weighed against the uncertainties about serious harms and enhanced recovery to inform the careful selection of surgical patients. Recommendations for future research are proposed.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".