Is an abdominal cerclage indicated with a cervical myomectomy? A case report
Bibliographic record
Abstract
INTRODUCTION: Cervical myomectomy can compromise cervical integrity and the risk of subsequent cervical incompetence is unclear. In this case report, the literature on cervical myomectomies is reviewed as well as that on the potential benefits of cervical cerclage. CASE PRESENTATION: A 30-year-old woman, nulligravida, with a 12 cm cervical leiomyoma consulted for heavy menstrual bleeding and pelvic pain. After failure of multiple medical therapies, a laparoscopic cervical myomectomy was successfully performed after pre-operative uterine artery embolization using absorbable gelatin sponges to reduce surgical blood loss. DISCUSSION: A concomitant laparoscopic cerclage was achieved in order to prevent cervical incompetence, given that the full thickness of the anterior cervix was penetrated during the myomectomy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".