Racial disparities in fetal and infant outcomes: a multiple-decrement life table approach
Bibliographic record
Abstract
We discuss different frameworks to conceptualize age in the gestational period and in the first year of life, and then apply two conceptualizations to quantify racial/ethnic differences on a range of different fetal and infant outcomes in the United States. In particular, we focus on the extended approach, which combines gestational age and age in the first year of life onto a continuum of adjusted age since conception, and the gestational approach, which takes the viewpoint of gestation but considers both fetal outcomes and eventual outcomes in the first year of life. Both approaches use a multiple-decrement life table framework, accounting for decrements that related to all potential outcomes, including birth, fetal death, and infant death or survival. We find that the risks of both fetal and infant death are non-linear over the gestational age period and early weeks of life, with the highest risks at the beginning of the period (at 20 weeks) but also at around 37-40 weeks. The relative risks of different race/ethnic groups also change across age; in particular, the non-Hispanic Black population has a heightened risk of all adverse outcomes, but the magnitude of the difference in risk depends on gestational age, and in general, disparities are lowest in the mid-range of gestational ages.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".