Optimal birth weight percentile cut‐offs in defining small‐ or large‐for‐gestational‐age
Bibliographic record
Abstract
AIMS: It remains questionable what birth weight for gestational age percentile cut-offs should be used in defining clinically important poor or excessive foetal growth. We aimed to evaluate the optimal birth weight percentile cut-offs for defining small- or large-for-gestational-age (SGA or LGA). METHODS: In a birth cohort-based analysis of 17 979 120 non-malformation singleton live births, U.S. 1995-2001, we assessed the optimal birth weight percentile cut-offs for defining SGA and LGA. The 25th-75th percentile group served as the reference. Primary outcomes are the risk ratios (RR) of neonatal death and low 5-min Apgar score (<4) comparing SGA or LGA versus the reference group. More than 2-fold risk elevations were considered clinically significant. RESULTS: The 15th birth weight cut-off already identified SGA infants at more than 2-fold risk of neonatal death at pre-term, term or post-term, except for extremely pre-term births <28 weeks (continuous risk reductions over increasing birth weight percentiles). LGA was associated with a reduced risk of low 5-min Apgar score at pre-term, but an elevated risk at term and post-term. The 97th cut-off identified LGA infants at 2-fold risk of low 5-min Apgar at term. CONCLUSION: The commonly used 10th and 90th birth weight percentile cut-offs for defining SGA and LGA respectively seem largely arbitrary. The 15th and 97th percentiles may be the optimal cut-offs to define SGA and LGA respectively.
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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 | 0.013 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".