Does one size fit all? The case for ethnic-specific standards of fetal growth
Bibliographic record
Abstract
BACKGROUND: Birth weight for gestational age is a widely-used proxy for fetal growth. Although the need for different standards for males and females is generally acknowledged, the physiologic vs pathologic nature of ethnic differences in fetal growth is hotly debated and remains unresolved. METHODS: We used all stillbirth, live birth, and deterministically linked infant deaths in British Columbia from 1981 to 2000 to examine fetal growth and perinatal mortality in Chinese (n = 40,092), South Asian (n = 38,670), First Nations, i.e., North American Indian (n = 56,097), and other (n = 731,109) births. We used a new analytic approach based on total fetuses at risk to compare the four ethnic groups in perinatal mortality, mean birth weight, and "revealed" (< 10th percentile) small-for-gestational age (SGA) among live births based on both a single standard and four ethnic-specific standards. RESULTS: Despite their lower mean birth weights and higher SGA rates (when based on a single standard), Chinese and South Asian infants had lower perinatal mortality risks throughout gestation. The opposite pattern was observed for First Nations births: higher mean birth weights, lower revealed SGA rates, and higher perinatal mortality risks. When SGA was based on ethnic-specific standards, however, the pattern was concordant with that observed for perinatal mortality. CONCLUSION: The concordance of perinatal mortality and SGA rates when based on ethnic-specific standards, and their discordance when based on a single standard, strongly suggests that the observed ethnic differences in fetal growth are physiologic, rather than pathologic, and make a strong case for ethnic-specific standards.
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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 | 0.053 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".