Bibliographic record
Abstract
OBJECTIVE: To estimate the benefits and harms of misoprostol use for cervical dilation in patients undergoing operative hysteroscopy. DATA SOURCES: We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (from inception to February 2011). We also searched trial registries, other sources of unpublished or gray literature, and the reference lists of retrieved studies. METHODS OF STUDY SELECTION: Randomized controlled trials (RCTs) of patients undergoing operative hysteroscopy that used misoprostol compared with placebo were included. TABULATION, INTEGRATION, AND RESULTS: The two coauthors independently screened search results for inclusion, assessed trials for methodologic quality, extracted data, and resolved disagreements through discussion. A total of seven RCTs with 568 patients met inclusion criteria. The quality of evidence for all outcomes was low. The pooled estimate did not rule out a beneficial effect of misoprostol on cervical dilation (six studies, 506 participants; mean difference 0.85 mm, 95% confidence interval [CI] -0.58 to 2.27). The pooled estimate did not rule out a beneficial effect of misoprostol on surgical complications (cervical lacerations, uterine perforations, and false passages [seven studies, 545 patients, pooled relative risk [RR] 0.65, 95% CI 0.19-2.26]). There was an increase in side effects (cramps, vaginal bleeding, nausea, and diarrhea) in the misoprostol group (four studies, 374 patients; RR 4.28, 95% CI 1.43-12.85). The number needed to harm to have one patient with preoperative vaginal bleeding was six, for diarrhea was seven, and for nausea was 13. CONCLUSION: This review did not rule out a beneficial effect of misoprostol on cervical dilation or surgical complications. There was an increase in side effects in operative hysteroscopy patients treated with misoprostol. Current evidence does not support the routine use of preoperative misoprostol in operative hysteroscopy.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".