Association Between Unintentional Injury During Pregnancy and Excess Risk of Preterm Birth and Its Neonatal Sequelae
Bibliographic record
Abstract
The sequelae of preterm births may differ, depending on whether birth follows an acute event or a chronic condition. In a population-based cohort study of 2,711,645 Canadian hospital deliveries from 2003 to 2012, 3,059 women experienced unintentional injury during pregnancy. We assessed the impact of the acute event on pregnancy outcome and on neonatal complications, such as nontraumatic intracranial hemorrhage, respiratory distress syndrome, intubation, and death. We adjusted for maternal age, parity, pregnancy conditions, and (for neonates) gestational age in logistic regression analyses. Injury was significantly associated with fetal mortality and early preterm delivery. For preterm infants born to injured women during the hospitalization for injury versus those born to noninjured women, the adjusted odds ratios were 2.25 (95% confidence interval (CI): 1.23, 4.17) for neonatal death, 2.44 (95% CI: 1.76, 3.37) for respiratory distress, 2.20 (95% CI: 1.26, 3.84) for nontraumatic intracranial hemorrhage, and 2.17 (95% CI: 1.60, 2.96) for intubation, despite more favorable fetal growth in those born to noninjured women (adjusted birth-weight-for-gestational-age z score: 0.154 vs. 0.024, P = 0.041; small-for-gestational-age rate: 4.5% vs. 9.5%, P = 0.001). Our findings suggest that adaptation to the suboptimal intrauterine environment underlying chronic causes of preterm birth may protect preterm infants from adverse sequelae.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".