Reducing Postoperative Pain After Tubal Ligation With Rings or Clips
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of using local anesthesia during interval laparoscopic tubal ligation to control postoperative pain. DATA SOURCES: We searched MEDLINE, PubMed, and Cochrane databases and found additional articles from bibliographies of relevant studies. METHODS OF STUDY SELECTION: We included only randomized, double-blind, placebo-controlled trials reporting postoperative pain after interval laparoscopic tubal ligation under general anesthesia (n=20). The trials compared the application of topical or injectable local anesthetic with placebo and used a visual analog scale (VAS) (scores 0-100) or the Modified McGill Pain Intensity Scale (subsequently converted to a VAS) to assess pain. TABULATION, INTEGRATION, AND RESULTS: Pain scores were evaluated at the following times after extubation: 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 24 hours. The meta-analysis was based on random-effects methods for pooled data using RevMan. Postoperative pain decreased with the use of local anesthetic compared with placebo as follows (mean VAS decrease in millimeters, 95% confidence interval): 30 minutes 18.6 (11.7-25.5); 1 hour 16.6 (9.3-24.0); 2 hours 17.4 (9.6-25.2); 4 hours 12.5 (5.1-19.9); 8 hours 11.9 (6.7-17.1); and 24 hours 3.9 (-1.4 to 9.2). There was moderate heterogeneity in the data across studies (I statistic ranging from 55% to 75%). The effect size was similar for the following subgroups: pain scores reported as means or medians and use of McGill compared with VAS pain scales. A stratified analysis of trials including ring tubal ligation compared with clip tubal ligation showed the use of local anesthetic decreased pain substantially for both. No eligible studies assessed tubal ligation with cautery. CONCLUSION: Use of local anesthetic during laparoscopic tubal ligation substantially reduces postoperative pain up to 8 hours after surgery.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".