Fostering Excellence in Obstetrical Surgery
Bibliographic record
Abstract
R Douglas Wilson Department of Obstetrics and Gynecology, Cumming School of Medicine University of Calgary, Calgary, Alberta, CanadaCorrespondence: R Douglas Wilson, Department of Obstetrics and Gynecology, Cumming School of Medicine University of Calgary, Alberta, Canada, Tel +1 403-470-7229, Email doug.wilson@ahs.caIntroduction: This obstetric surgery review is directed toward the common obstetrical surgeries (caesarean delivery, VBAC/TOLAC, operative vaginal delivery, placenta accreta spectrum) with evidence for quality and safety to allow for obstetrical outcome excellence.Materials and Methods: This focused scoping review has used a structured process for article identification and inclusion for each of the focused surgeries.Results: The review results provide an obstetrical surgery (OS) overview for caesarean delivery, vaginal birth after caesarean delivery and/or trial of labor after caesarean delivery, operative vaginal delivery, placenta accreta spectrum; considerations for quality and safety variance due to non-clinical human factors; quality improvement (QI) tools; OS QI implementation cohorts; implementation considering certain barriers and solutions.Conclusion: Administrative health care systems and obstetrical surgery care providers cannot afford, not to consider and implement, certain evidenced-based “bottom-up/top-down” processes for quality and safety, as the patients will demand the quality and the safety, but the lawyers should not have to enforce it.Keywords: obstetrical safety, obstetrical quality, obstetrical morbidity, caesarean delivery, vaginal birth after caesarean delivery, trial of labor after caesarean delivery, operative vaginal delivery, placenta accreta spectrum, implementation process
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".