Fetuses-at-risk, to avoid paradoxical associations at early gestational ages: extension to preterm infant mortality
Bibliographic record
Abstract
BACKGROUND: Fetuses-at-risk denominators are commonly used in research on preterm stillbirth, but applications to postnatal outcomes such as preterm infant mortality are controversial. We evaluated whether biased associations between maternal risk factors and preterm infant mortality caused by stratification by preterm birth could be avoided using fetuses-at-risk risk ratios. METHODS: Data included 3 277 570 births drawn from the linked live birth-death file for Canada from 1990 through 2005. We used maternal age as the risk factor, and estimated the association with stillbirth, early neonatal, late neonatal and postneonatal mortality by gestational interval (22-24, 25-27, 28-31, 32-36, ≥37 weeks). Models were run using (i) log-binomial regression stratified by preterm gestational age, and (ii) unstratified log-binomial regression using fetuses-at-risk denominators. RESULTS: Extremes of maternal age were associated with higher mortality among term births. Among preterm births, the stratified model suggested a protective, null or attenuated association of extremes of maternal age with stillbirth, early, late and post neonatal mortality. The unstratified fetuses-at-risk model, however, resulted in the expected higher risk of mortality at extremes of maternal age for all outcomes. CONCLUSIONS: Fetuses-at-risk regression can avoid paradoxical associations between maternal exposures and mortality of infants born early in gestation, caused by preterm birth stratification bias. The fetuses-at-risk approach can be extended through the first year of life, or potentially beyond, depending on the outcome and presence of unmeasured confounders associated with preterm birth.
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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 | 0.035 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".