Operating time and blood loss during laparoscopic‐assisted vaginal hysterectomy with in situ morcellation
Bibliographic record
Abstract
OBJECTIVE: To develop a regression-based prediction equation for operative time and estimated blood loss in laparoscopically assisted vaginal hysterectomy (LAVH) for large uteri, as required, by combined laparoscopic in situ and vaginal morcellation. DESIGN: Prospective study (Canadian Task Force classification II-1). SETTING: University-affiliated hospitals. SAMPLE: Fifty-six patients who underwent LAVH. Methods. Evaluation of all patients who had LAVH with laparoscopic in situ morcellation and vaginal morcellation during a 2-year period. MAIN OUTCOME MEASURES: Operative time, estimated blood loss, total uterine weight by laparoscopic or vaginal morcellation, complications and length of hospital stay. RESULTS: Mean operative time was 133 ± 22 minutes, and mean blood loss 133 ± 101 ml. Mean uterine weight was 383 ± 187 g by laparoscopic and 251 ± 103 g by vaginal morcellation. Greater total uterine weight and morcellation were associated with longer operative times. Blood loss correlated with uterine weight when vaginal morcellation was also used. A regression equation is presented for estimating the likely operating time and blood loss. CONCLUSIONS: An increase in the operative time and a higher blood loss can be expected as the uterine weight increases and can be predicted taking morcellation methods into account.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".