US Black vs White disparities in foetal growth: physiological or pathological?
Bibliographic record
Abstract
BACKGROUND: Birthweight for gestational age is lower in US Black infants than in US White infants. It is unknown, however, whether this difference is 'normal' (i.e. physiological) or reflects pathological foetal growth restriction. METHODS: We applied an analytic approach based on foetuses at risk to compare gestational age-specific rates of live birth, 'revealed' small-for-gestational-age (SGA), and neonatal mortality among singleton infants >or=22 weeks of gestation and >or=500 g born in 1998-2000 to US White (n = 9 012 194), US-born Black (n = 1 554 382), and foreign-born Black (n = 200 395) mothers. Graphical methods and Cox proportional hazards regression analyses were used to compare outcomes in the three ethnic groups. RESULTS: Rates of live birth and neonatal mortality were highest at all gestational ages in US-born Blacks, lowest in Whites, and intermediate in foreign-born Blacks. The revealed SGA pattern cohered much more closely with the observed pattern for neonatal mortality when SGA was defined based on a single, overall standard of birthweight for gestational age than when based on ethnic group-specific standards. CONCLUSION: The closer coherence of revealed SGA and neonatal mortality rates based on a single standard and the intermediate pattern among foreign-born Blacks strongly suggest that Black-White differences in birthweight for gestational age are pathological, rather than physiological.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".