Does maternal stature modify the association between infants who are small or large for gestational age and adverse perinatal outcomes? A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of maternal stature on adverse birth outcomes and quantify perinatal risks associated with small- and large-for-gestational age infants (SGA and LGA, respectively) born to mothers of short, average, and tall stature. DESIGN: Retrospective cohort study. SETTING: USA, 2016-2017. POPULATION: Women with a singleton live birth (N = 7 325 741). METHODS: Using data from the National Center for Health Statistics, short and tall stature were defined as <10th and >90th centile of the maternal height distribution. Modified Poisson regression was used to estimate adjusted risk ratios (aRRs) and 95% confidence intervals (95% CIs). MAIN OUTCOME MEASURES: Preterm birth (<37 weeks of gestation), neonatal intensive care unit (NICU) admission and severe neonatal morbidity/mortality (SNMM). RESULTS: With increased maternal height, the risk of adverse outcomes increased in SGA infants and decreased in LGA infants compared with infants appropriate-for-gestational age (AGA) (p < 0.001). Infants who were SGA born to women of tall stature had the highest risk of NICU admission (aRR 1.98, 95% CI 1.91-2.05; p < 0.001), whereas LGA infants born to women of tall stature had the lowest risk (aRR 0.85, 95% CI 0.82-0.88; p < 0.001), compared with AGA infants born to women of average stature. LGA infants born to women of short stature had an increased risk of NICU admission and SNMM, compared with AGA infants born to women of average stature (aRR 1.32, 95% CI 1.27-1.38; aRR 1.21, 95% CI 1.13-1.29, respectively). CONCLUSIONS: Maternal height modifies the association between SGA and LGA status at birth and neonatal outcomes. This quantification of risk can assist healthcare providers in monitoring fetal growth, and optimising neonatal care and follow-up.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".