A parsimonious explanation for intersecting perinatal mortality curves: understanding the effects of race and of maternal smoking
Bibliographic record
Abstract
BACKGROUND: Neonatal mortality rates among black infants are lower than neonatal mortality rates among white infants at birth weights <3000 g, whereas white infants have a survival advantage at higher birth weights. This finding is also observed when birth weight-specific neonatal mortality rates are compared between infants of smokers and non-smokers. We provide a parsimonious explanation for this paradoxical phenomenon. METHODS: We used data on births in the United States in 1997 after excluding those with a birth weight <500 g or a gestational age <22 weeks. Birth weight- and gestational age-specific perinatal mortality rates were calculated per convention (using total live births at each birth weight/gestational age as the denominator) and also using the fetuses at risk of death at each gestational age. RESULTS: Perinatal mortality rates (calculated per convention) were lower among blacks than whites at lower birth weights and at preterm gestational ages, while blacks had higher mortality rates at higher birth weights and later gestational ages. With the fetuses-at-risk approach, mortality curves did not intersect; blacks had higher mortality rates at all gestational ages. Increases in birth rates and (especially) growth-restriction rates presaged gestational age-dependent increases in perinatal mortality. Similar findings were obtained in comparisons of smokers versus nonsmokers. CONCLUSIONS: Formulating perinatal risk based on the fetuses-at-risk approach solves the intersecting perinatal mortality curves paradox; blacks have higher perinatal mortality rates than whites and smokers have higher perinatal mortality rates than nonsmokers at all gestational ages and birth weights.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".