Time-related Bias When Studying Perinatal Complications After Maternal Injuries: Application to Maternal Injuries and Preterm Birth
Bibliographic record
Abstract
BACKGROUND: Some studies examining associations between maternal injuries and preterm birth reported null or counterintuitive protective effects, especially for 3rd-trimester injuries, likely due to time-related biases. METHODS: This retrospective cohort study comprised all births occurring at the Atrium Health Wake Forest Baptist health system between 2018 and 2024. We ascertained maternal injuries using validated diagnostic codes and defined preterm birth as gestational age at delivery <37 weeks. We estimated associations between maternal injuries and preterm birth with two approaches. We used logistic regression for time-fixed analysis (injury at any point in pregnancy yes/no and preterm birth yes/no) and Cox proportional hazards models for time-varying analysis (i.e., time-varying injury definition, restricted follow-up to periods when pregnancies were at risk of preterm birth). RESULTS: Among 58,897 births, 1,801 women (3.1%) experienced maternal injuries during pregnancy. With the time-varying approach, maternal injuries were associated with increased risk of preterm birth (adjusted hazard ratio [HR]: 1.16; 95% confidence interval [CI] = 1.01, 1.32). Trimester-specific analyses showed positive associations for all trimesters, with higher effect estimates observed for 2nd and 3rd trimester injuries (adjusted HRs: 1.17; 95% CI = 0.97, 1.42) and 1.22 (95% CI = 0.92, 1.61), respectively. With time-fixed analyses, associations for any injury were underestimated, compared with time-varying analyses, and results for 3rd trimester injuries showed counterintuitive negative associations (adjusted odds ratio: 0.73 [0.54, 0.98]). CONCLUSIONS: Time-related biases typically underestimate associations between maternal injuries and preterm birth, particularly for 3rd - trimester injuries. Rigorous study design and analytical methods that account for time-related biases are crucial in studies investigating adverse outcomes after maternal injuries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".