External Cephalic Version: A Retrospective Chart Review at a Canadian Tertiary Care Centre
Bibliographic record
Abstract
OBJECTIVES: The primary objective is to identify our local external cephalic version (ECV) success rate, variables associated with increased likelihood of success, and complication rates. The secondary objective is to allow obstetrical care providers to accurately counsel patients undergoing a trial of ECV. METHODS: We analysed patient charts between January 2018 and December 2022 who underwent ECV. Variables included maternal age, parity, gestational age at the time of ECV attempt, breech type, anesthetic, uterine relaxant, placental location, neonatal birthweight, and provider seniority. Outcomes were ECV success, mode of delivery, emergent cesarean delivery rate due to ECV, and neonatal intensive care unit admission. Appropriate statistical analysis was performed. RESULTS: Overall, 258 patients were included. Overall success rate was 31%. Multiparity, transverse presentation, and neonatal birthweight >3.3 kg were associated with significantly increased success rates. Uterine relaxant use was associated with a lower success rate than no relaxant use, which is potentially explained by significantly more frequent relaxant use in non-transverse presentations and a non-significant trend in increased relaxant use in primiparous patients. Other factors including anesthetic use, maternal age, gestational age, placental location, and provider seniority did not significantly impact success. The emergency cesarean delivery rate was 10% and the neonatal intensive care unit admission rate was 8%, both of which were higher than anticipated. CONCLUSIONS: ECV remains an option for the management of the term breech. Obstetrical providers at our centre and in others may use this study to more accurately counsel patients using local data and optimize the likelihood of success based on patient and peri-procedural factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".