Maternal and neonatal trauma following operative vaginal delivery
Bibliographic record
Abstract
BACKGROUND: Operative vaginal delivery (OVD) is considered safe if carried out by trained personnel. However, opportunities for training in OVD have declined and, given these shifts in practice, the safety of OVD is unknown. We estimated incidence rates of trauma following OVD in Canada, and quantified variation in trauma rates by instrument, region, level of obstetric care and institutional OVD volume. METHODS: We conducted a cohort study of all singleton, term deliveries in Canada between April 2013 and March 2019, excluding Quebec. Our main outcome measures were maternal trauma (e.g., obstetric anal sphincter injury, high vaginal lacerations) and neonatal trauma (e.g., subgaleal hemorrhage, brachial plexus injury). We calculated adjusted and stabilized rates of trauma using mixed-effects logistic regression. RESULTS: Of 1 326 191 deliveries, 38 500 (2.9%) were attempted forceps deliveries and 110 987 (8.4%) were attempted vacuum deliveries. The maternal trauma rate following forceps delivery was 25.3% (95% confidence interval [CI] 24.8%-25.7%) and the neonatal trauma rate was 9.6 (95% CI 8.6-10.6) per 1000 live births. Maternal and neonatal trauma rates following vacuum delivery were 13.2% (95% CI 13.0%-13.4%) and 9.6 (95% CI 9.0-10.2) per 1000 live births, respectively. Maternal trauma rates remained higher with forceps than with vacuum after adjustment for confounders (adjusted rate ratio 1.70, 95% CI 1.65-1.75) and varied by region, but not by level of obstetric care. INTERPRETATION: In Canada, rates of trauma following OVD are higher than previously reported, irrespective of region, level of obstetric care and volume of OVD among hospitals. These results support a reassessment of OVD safety in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".