Individualized sex-specific birth weight percentiles for gestational age based on maternal height and weight
Bibliographic record
Abstract
OBJECTIVES: The maternal body size affects birth weight. The impact on birth weight percentiles is unknown. The objective of the study was to develop birth weight percentiles based on maternal height and weight. METHODS: This observational study analyzed 2.2 million singletons from the German Perinatal Survey. Data were stratified into 18 maternal height and weight groups. Sex-specific birth weight percentiles were calculated from 31 to 42 weeks and compared to percentiles from the complete dataset using the GAMLSS package for R statistics. RESULTS: Birth weight percentiles not considering maternal size showed 22% incidence of small for gestational age (SGA) and 2% incidence of large for gestational age (LGA) for the subgroup of newborns from petite mothers, compared to a 4% SGA and 26% LGA newborns from big mothers. The novel percentiles based on 18 groups stratified by maternal height and weight for both sexes showed significant differences between identical original percentiles. The differences were up to almost 800 g between identical percentiles for petite and big mothers. The 97th and 50th percentile from the group of petite mothers almost overlap with the 50th and 3rd percentile from the group of big mothers. CONCLUSIONS: There is a clinically significant difference in birth weight percentiles when stratified by maternal height and weight. It could be hypothesized that birth weight charts stratified by maternal anthropometry could provide higher specificity and more individual prediction of perinatal risks. The new percentiles may be used to evaluate estimated fetal as well as birth weight.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".