Identifying Birth Weight Cutoffs Based on Maternal Height and Apgar scores
Bibliographic record
Abstract
The birth weight cutoff suggested by the World Health Organization (under 2500g) fails to reflect health risk across populations of diverse ethnicities. Based on that, previous studies have suggested using other indicators, such as maternal height and infant sex, to derive more accurate low birth weight (LBW) cutoffs. However, such approaches have not considered fetal well-being when deriving the cutoffs. Therefore, this study addresses this limitation with a novel approach using Apgar scores to derive LBW cutoffs based on maternal height. We used the 2022 CDC birth dataset to implement a two-stage analytical approach. The first stage used a Conditional Inference Tree (CIT) and a Fuzzy Inference Model (FIM) to identify combinations of maternal height and birth weight values associated with low Apgar scores. The second stage employed an ensemble of five machine learning regressors to estimate birth weight thresholds associated with normal Apgar scores. Our experimental results indicate that adaptive cutoffs outperform the fixed 2500-gram WHO cutoff. Specifically, the WHO cutoff does not effectively scale; its ability to detect newborns with low Apgar scores diminishes as maternal height increases. Overall, this research contributes to perinatal assessment by offering a method for identifying at-risk newborns based on maternal height and infant sex.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".