Developing adjustable birth weight cutoffs based on maternal height and Apgar scores
Bibliographic record
Abstract
Birth weight is crucial for evaluating an infant's health and nutritional status. The World Health Organization (WHO) defines low birth weight (LBW) as being under 2,500 g, considering it a risk condition. However, this standard may not apply universally because it was derived using a European-descendant population. To address this, previous research has recommended using ethnicity-specific birth weight charts, but with globalization and mixed-race families rising, ethnicity-specific birth weight charts may not be reliable. Therefore, alternative measurements to identify at-risk newborns are needed. This study addresses this limitation using Apgar scores to derive LBW cutoffs based on maternal height, an approach that has not been previously explored. To that aim, using the 2022 CDC birth dataset, we implemented a two-stage analytical approach. First, a conditional inference tree and a fuzzy inference model were applied to explore the relationship between maternal height, birth weight, and Apgar scores. In the second stage, we employed an ensemble of five machine learning regressors to estimate birth weight thresholds associated with normal Apgar scores. Our findings suggest that under the fixed WHO LBW standard, the probability of achieving a normal Apgar score decreases as maternal height increases. This finding suggests that some newborns above the standard LBW cutoff may still face health risks. An adjustable LBW threshold based on maternal height is essential for more accurately assessing newborn health and ensuring better outcomes.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".