Risk Factors Associated with Uterine Rupture and Dehiscence: A Cross-Sectional Canadian Study
Bibliographic record
Abstract
Abstract Objective To compare maternal and perinatal risk factors associated with complete uterine rupture and uterine dehiscence. Methods Cross-sectional study of patients with uterine rupture/dehiscence from January 1998 to December 2017 (30 years) admitted at the Labor and Delivery Unit of a tertiary teaching hospital in Canada. Results There were 174 (0.1%) cases of uterine disruption (29 ruptures and 145 cases of dehiscence) out of 169,356 deliveries. There were associations between dehiscence and multiparity (odds ratio [OR]: 3.2; p = 0.02), elevated maternal body mass index (BMI; OR: 3.4; p = 0.02), attempt of vaginal birth after a cesarian section (OR: 2.9; p = 0.05) and 5-minute low Apgar score (OR: 5.9; p < 0.001). Uterine rupture was associated with preterm deliveries (36.5 ± 4.9 versus 38.2 ± 2.9; p = 0.006), postpartum hemorrhage (OR: 13.9; p < 0.001), hysterectomy (OR: 23.0; p = 0.002), and stillbirth (OR: 8.2; p < 0.001). There were no associations between uterine rupture and maternal age, gestational age, onset of labor, spontaneous or artificial rupture of membranes, use of oxytocin, type of uterine incision, and birthweight. Conclusion This large cohort demonstrated that there are different risk factors associated with either uterine rupture or dehiscence. Uterine rupture still represents a great threat to fetal-maternal health and, differently from the common belief, uterine dehiscence can also compromise perinatal outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".