P.121 Preoperative predictors of poor postoperative pain control: systematic review and meta-analysis
Bibliographic record
Abstract
Background: Inadequate postoperative pain control is common and is associated with negative clinical outcomes. The objective is to identify preoperative predictors of poor postoperative pain control in the adult population undergoing inpatient surgery. Methods: Meta-analysis was performed according to MOOSE guidelines. Studies were included if they evaluated postoperative pain using a validated instrument in adults undergoing inpatient surgery and reported a measure of association between poor postoperative pain control and at least one preoperative predictor. Measures of association were pooled using random effects models. Results: A total of 33 studies representing 59,259 patients were included. Significant preoperative predictors of poor postoperative pain included sleeping difficulties (OR 2.32 [95% CI 1.46-3.69]), history of depressive symptoms (OR 1.71 [95% CI 1.32-2.22]), use of preoperative analgesia (OR 1.54 [95% CI 1.18-2.03]), smoking (OR 1.33 [95% CI 1.09-1.61]), -female sex (OR 1.29 [95% CI 1.17-1.43]), presence of preoperative pain (OR 1.21 [95% CI 1.10-1.32]], history of anxiety symptoms (OR 1.22 [95% CI 1.09-1.36)], younger age (OR 1.18 [95% CI 1.05-1.32)], and higher BMI (OR 1.02 [95% CI 1.01-1.03]). Conclusions: Nine significant predictors of poor postoperative pain control were identified and these should be recognized as important factors when developing pre- and peri-operative strategies to improve pain outcomes.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.005 | 0.008 |
| 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) | 0.020 | 0.001 |
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".