Maternal characteristics of pregnancies with intrauterine fetal demise
Bibliographic record
Abstract
OBJECTIVE: To identify maternal characteristics independently associated with pregnancies resulting in intrauterine fetal demise (IUFD). STUDY DESIGN: This was a population-based cohort study of all births taking place at the McGill University Health Centre in Montreal, Canada, between 2001 and 2007, using the McGill University Obstetrics and Neonatal Database. Maternal characteristics were compared between pregnancies that resulted in IUFD and control pregnancies resulting in live newborns. A logistic regression analysis was constructed to identify parameters independently associated with IUFD. RESULTS: We identified 20,744 births during the study period, 87 of which were complicated by IUFD. Mothers with IUFD were more likely to be younger, with less formal education, higher rates of smoking during pregnancy, and more fetal anomalies (42.5% vs. 7.5%, P<0.001). After exclusion of pregnancies with congenital and/or chromosomal abnormalities, less formal education (7 vs. 13.6 school years, P<0.001) and smoking during pregnancy (24% vs. 7.7%, P<0.001) remained significantly more common in pregnancies resulting in IUFD. In the multivariable regression analysis both smoking and number of maternal school years were independently associated with IUFD pregnancies (OR 2.22 for smoking, P=0.007 and OR 0.865 for number of school years, P<0.001). CONCLUSION: Lower levels of education and smoking during pregnancy are independent predictors of IUFD.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".