Laparoscopic-Assisted Myomectomy with Bilateral Uterine Artery Occlusion/Ligation
Bibliographic record
Abstract
STUDY OBJECTIVE: Conventional laparoscopic myomectomy (CLM) and robotic-assisted myomectomy (RAM) are limited in the number and size of myomas that can be removed, whereas abdominal myomectomy (AM) is associated with increased complications and morbidity. Here we evaluated the surgical outcomes of these myomectomy techniques compared with those of laparoscopic-assisted myomectomy (LAM), a hybrid approach that combines laparoscopy and minilaparotomy with bilateral uterine artery occlusion or ligation to control blood loss. DESIGN: Retrospective chart review (Canadian Task Force classification II-1). SETTING: Suburban community hospital. PATIENTS: Women age ≥18 years with nonmalignant indications. INTERVENTION: A total of 1313 consecutive CLMs, RAMs, AMs, and LAMs performed between January 2011 and December 2013. MEASUREMENTS AND MAIN RESULTS: Our review included 163 CLMs (12%), 156 RAMs (12%), 686 AMs (52%), and 308 LAMs (23%). Although the average number, size, and total weight of leiomyomas removed were comparable in the LAM and AM groups (9.1, 8.13 cm, and 391 g, respectively, vs 9.0, 7.5 cm, and 424 g; p < .0001), the number and weight of myomas were significantly greater in those 2 groups compared with the CLM and RAM groups (2.9 and 217 g, respectively, and 2.9 and 269 g; p < .0001). The intraoperative complication rate was highest in the RAM group, and the postoperative complication rate was highest in the AM group, both of which were approximately 3 times greater than the rates in the LAM group. There was no statistically significant difference in postoperative complication rates between the CLM and LAM groups. CONCLUSION: LAM with uterine artery occlusion/ligation is a viable approach for removing large tumor loads while minimizing blood loss and precluding the need for power morcellation.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".