Managing postoperative pain in adult outpatients: a systematic review and meta-analysis comparing codeine with NSAIDs
Bibliographic record
Abstract
BACKGROUND: Analgesics that contain codeine are commonly prescribed for postoperative pain, but it is unclear how they compare with nonopioid alternatives. We sought to compare the effectiveness of codeine and nonsteroidal anti-inflammatory drugs (NSAIDs) for adults who underwent outpatient surgery. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials comparing codeine and NSAIDs for postoperative pain in outpatient surgery. We searched MEDLINE and Embase from inception to October 2019 for eligible studies. Our primary outcome was the patient pain score, converted to a standard 10-point intensity scale. Our secondary outcomes were patient-reported global assessments and adverse effects. We used random-effects models and grading of recommendations assessment, development and evaluation (GRADE) to assess the quality of evidence. RESULTS: Forty studies, including 102 trial arms and 5116 patients, met inclusion criteria. The studies had low risk of bias and low-to-moderate heterogeneity. Compared with codeine, NSAIDs were associated with better pain scores at 6 hours (weighted mean difference [WMD] 0.93 points, 95% confidence interval [CI] 0.71 to 1.15) and at 12 hours (WMD 0.79, 95% CI 0.38 to 1.19). Stronger NSAID superiority at 6 hours was observed among trials where acetaminophen was coadministered at equivalent doses between groups (WMD 1.18, 95% CI 0.87 to 1.48). NSAIDs were associated with better global assessments at 6 hours (WMD -0.88, 95% CI -1.04 to -0.72) and at 24 hours (WMD -0.67, 95% CI -0.95 to -0.40), and were associated with fewer adverse effects, including bleeding events. INTERPRETATION: We found that adult outpatients report better pain scores, better global assessments and fewer adverse effects when their postoperative pain is treated with NSAIDs than with codeine. Clinicians across all specialties can use this information to improve both pain management and opioid stewardship.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".