Economic and Survival Implications of Use of Electric Power Morcellation for Hysterectomy for Presumed Benign Gynecologic Disease
Bibliographic record
Abstract
BACKGROUND: Electric power morcellation during laparoscopic hysterectomy allows some women to undergo minimally invasive surgery but may disrupt underlying occult malignancies and increase the risk of tumor dissemination. METHODS: We developed a state transition Markov cohort simulation model of the risks and benefits of hysterectomy (abdominal, laparoscopic, and laparoscopic with electric power morcellation) for women with presumed benign gynecologic disease. The model considered perioperative morbidity, mortality, risk of cancer and dissemination, and outcomes in women with an underlying malignancy. We explored the effectiveness from a societal perspective stratified by age (<40, 40-49, 50-59, and ≥60 years). RESULTS: Under all scenarios, modeled laparoscopic hysterectomy without morcellation was the most beneficial strategy. Laparoscopic hysterectomy with morcellation was associated with 80.83 more intraoperative complications, 199.64 fewer perioperative complications, and 241.80 fewer readmissions than abdominal hysterectomy per 10 000 women. Per 10 000 women younger than age 40 years, laparoscopic hysterectomy with morcellation was associated with 1.57 more cases of disseminated cancer and 0.97 fewer deaths than abdominal hysterectomy. The excess cases of disseminated cancer per 10 000 women with morcellation compared with abdominal hysterectomy increased with age to 47.54 per 10 000 in women age 60 years and older. Compared with abdominal hysterectomy, this resulted in 0.30 (age 40-49 years), 5.07 (age 50-59 years), and 18.14 (age 60 years and older) excess deaths per 10 000 women in the respective age groups. CONCLUSION: Laparoscopic hysterectomy without morcellation is the most beneficial approach of the three methods of hysterectomy studied. In older women, the risks of electric power morcellation may outweigh the benefits of minimally invasive hysterectomy.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".