Trainee Involvement at Vaginal Hysterectomy: A Canadian Multicentre Retrospective Review
Bibliographic record
Abstract
OBJECTIVES: This study aimed to describe postgraduate obstetrics and gynaecology trainee involvement at vaginal hysterectomy (VH) compared with laparoscopic hysterectomy (LH) and laparoscopic-assisted vaginal hysterectomies, and to explore the impact of trainee involvement on VH surgical outcomes. METHODS: This was a multicentre retrospective cohort study of minimally invasive surgical (MIS) hysterectomies conducted from 2016 to 2022. Patient, surgery, and surgeon characteristics were compared between types of MIS hysterectomy, and factors associated with VH were identified through multivariable logistic regression analysis. Trainee presence at each type of MIS hysterectomy was documented. Analysis of VH surgical outcomes was performed with primary exposure as first-assistant training level (staff, fellow, resident). RESULTS: We included 5246 hysterectomies, with 1491 VHs, 3352 LHs, and 403 laparoscopic-assisted vaginal hysterectomies. Most hysterectomies involved at least 1 trainee (78.0%), less commonly at VH than at LH (76.8% vs. 79.5%, P < 0.05). When comparing VH with LH, more fellow involvement (48.3% vs. 35.1%, P < 0.001) and less resident involvement (60% vs. 66.8%, P < 0.001) was observed. Among hysterectomies that residents were present at, 30.2% of VH cases were performed with residents alone, as opposed to 42.4% of LH cases (P < 0.001). After adjusting for confounding factors, VHs with residents as the first assistant, compared with staff, had longer operative time (151 vs. 102 minutes, P < 0.001), higher estimated blood loss (200 vs. 100 cc, P < 0.001), and a higher rate of postoperative complications or readmission within 30 days of surgery (28.9% vs. 15.4%, P < 0.01). CONCLUSIONS: Residents are less frequently involved at VH compared with LH. VH surgical outcomes were found to worsen when trainees were the first assistant as opposed to staff.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".