Impact of a High-Volume Gynecologic Surgeon Preceptor on Benign Laparoscopic Hysterectomy
Bibliographic record
Abstract
Objective: To compare operative times and surgical outcomes of women undergoing benign laparoscopic hysterectomy by general obstetrician–gynecologists (OB-GYNs) alone with those performed by general OB-GYNs in conjunction with a high-volume minimally invasive gynecologic surgeon preceptor. Design: This is a retrospective cohort study. (Canadian Task Force Classification II-2). Materials and Methods: A surgical preceptoring program for low-volume OB-GYNs was implemented in 2011 at an academic-affiliated community hospital. All women undergoing laparoscopic hysterectomy for benign disease between 2011 and 2013 are included. Results: A total of 391 laparoscopic hysterectomies were performed: 179 by low-volume OB-GYNs alone and 212 with the assistance of a preceptor. Laparoscopic hysterectomies performed with a preceptor had shorter operative times (128.4 vs. 105.4 minutes, p < 0.01). After adjusting for differences between groups, those performed in conjunction with a preceptor were 42 minutes faster. The group receiving surgery in conjunction with a preceptor had decreased rates of composite organ injury (6% vs. 1%, p < 0.01), ureteral injury (3% vs. 0%, p = 0.02), and conversion to open approach (5% vs. 0%, p < 0.01). The estimated blood loss was also significantly lower (163 vs. 134 mL, p = 0.03) and there were fewer intraoperative consultations (6% vs. 0.5%, p < 0.01) in the preceptor group. There was no difference in postoperative rates of blood transfusion, readmission, or reoperation. Conclusion: Women undergoing laparoscopic hysterectomy in conjunction with a high-volume minimally invasive gynecologic surgeon preceptor have improved outcomes, including shorter operative time, lower rate of organ injury and conversion to laparotomy fewer intraoperative consultations, and lower estimated blood loss than women undergoing laparoscopic hysterectomy by general OB-GYNs alone. (J GYNECOL SURG 36:115)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.007 | 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 teacher head, 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".