Trend of Uterine Rupture and Different Approaches of Myomectomy [35B]
Bibliographic record
Abstract
INTRODUCTION: Uterine rupture is an uncommon typically occurring during pregnancy or labor. One of the risk factors in uterine rupture is previous myomectomy either by laparoscopy or laparotomy. Due to regulation of FDA on myoma morcellation, many gynecologists have returned to myomectomy by laparotomy. The purpose of the study was to evaluate this shift in surgical trends and the possible subsequent changes in number of uterine ruptures over the years. METHODS: Retrospective cohort study using the HCUP-NIS database. Our cohort consisted of women, aged 18 – 40 years, who underwent myomectomy by laparoscopy or laparotomy, and who suffered uterine rupture during pregnancy or labor between 2005 and 2014. RESULTS: Of a total 54,146 myomectomies, there were 237 uterine ruptures. The mean age of the patients was 31.8 years, they were mainly Caucasians, had private insurance and had high income. The myomectomy was performed mainly by laparotomy (97.7%); and 2.3% by laparoscopy. There was a decrease of total number of myomectomies performed annually, from 6646 in 2005 to 4589 in 2014. The numbers of uterine ruptures per 1,000 myomectomies were 4.2 after laparotomy and 10.6 after laparoscopic approach. Laparoscopic myomectomy increased the risk of uterine rupture by 2.3 fold [OR 2.3, 95% CI 1.2–4.2, P=.008] compared to open myomectomy after adjustment for sociodemographic and clinical characteristics. CONCLUSION: The myomectomies trend changed in recent years with a predominance of laparotomies over laparoscopies. Laparoscopic myomectomy increases the chance for uterine rupture with over 2 folds compared to open approach. More studies are needed to clarify this matter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".