Maternal body height is a stronger predictor of birth weight than ethnicity: analysis of birth weight percentile charts
Bibliographic record
Abstract
Background Anthropometric parameters such as birth weight (BW) and adult body height vary between ethnic groups. Ethnic-specific percentile charts are currently being used for the assessment of newborns. However, due to globalization and interethnic families, it is unclear which charts should be used. A correlation between a mother's height and her child's BW (1 cm accounts for a 17 g increase in BW) has been observed. The study aims to test differences in small for gestational age (SGA) and large for gestational age (LGA) rates, employing BW percentile charts based on maternal height between ethnic groups. Methods This retrospective study of 2.3 million mother/newborn pairs analyzed BW, gestational age, sex, maternal height and ethnicity from the German perinatal survey (1995-2000). These data were stratified for maternal height (≤157, 158-163, 164-169, 170-175, ≥176 cm) and region of origin (Germany, Central and Northern Europe, North America, Mediterranean region, Eastern Europe, Middle East and North Africa, and Asia excluding Middle East). Percentile charts were calculated for each maternal height group. Results The average BW and maternal height differ significantly between ethnic groups. On current percentile charts, newborns of taller mothers (≥176 cm) have a low rate of SGA and a high rate of LGA, whereas newborns of shorter mothers (≤157 cm) have a high rate of SGA and a low rate of LGA. When the BW data are stratified based on the maternal height, mothers of similar height from different ethnic groups show similar average BWs, SGA and LGA rates. Conclusion Maternal body height has a greater influence on BW than maternal ethnicity. The use of BW percentile charts for maternal height should be considered.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".