The clinical performance and population health impact of birth weight-for-gestational age indices with regard to adverse neonatal outcomes in term infants
Bibliographic record
Abstract
Abstract Background Despite the recent creation of several birth weight-for-gestational age references and standards, none has proven superior. We identified birth weight-for-gestational age cut-offs, and corresponding United States population-based, Intergrowth 21 st and World Health Organization centiles associated with higher risks of adverse neonatal outcomes, and evaluated their ability to predict serious neonatal morbidity and neonatal mortality (SNMM). Methods and findings The study population comprised singleton live births at 37-41 weeks’ gestation in the United States, 2003-2017. Birth weight-specific SNMM, which included 5-minute Apgar score<4, neonatal seizures, assisted ventilation and neonatal death, was modeled by gestational week using penalized B-splines. We estimated the birth weights at which SNMM odds was minimized (and higher by 10%, 50% and 100%), and identified the corresponding population, Intergrowth 21 st and World Health Organization (WHO) centiles. We then evaluated the individual- and population-level performance of these cut-offs for predicting SNMM. The study included 40,179,663 live births at 37-41 weeks’ gestation and 991,486 SNMM cases. Among female singletons at 39 weeks’ gestation, SNMM odds was lowest at 3,203 g birth weight (population, Intergrowth and WHO centiles 40, 52 and 46, respectively), and 10% higher at 2,835 g and 3,685 g (population centiles 11 th and 82 nd , Intergrowth centiles 17 th and 88 th and WHO centiles 15 th and 85 th ). SNMM odds were 50% higher at 2,495 g and 4,224 g and 100% higher at 2,268 g and 4,593 g. Birth weight cut-offs were poor predictors of SNMM. For example, the birth weight cut-off associated with 10% higher odds of SNMM among female singletons at 39 weeks’ gestation resulted in a sensitivity of 12.5%, specificity of 89.4% and population attributable fraction of 2.1%, while the cut-off associated with 50% higher odds resulted in a sensitivity of 2.9%, specificity of 98.4% and population attributable fraction of 1.3%. Conclusions Birth weight-for-gestational age cut-offs and centiles perform poorly when used to predict adverse neonatal outcomes in individual infants, and the population impact associated with these cut-offs is also small. Funding Canadian Institutes of Health Research (MOP-67125 and PJT153439). Author summary Why was this study done Despite the recent creation of several birth weight-for-gestational age references and standards, no method has proved superior for identifying small-for-gestational age (SGA), appropriate-for-gestational age (AGA) and large-for-gestational age (LGA) infants. For instance, infants classified as AGA by the Intergrowth Project 21 st standard and SGA by national references have a higher risk of perinatal death compared with infants deemed AGA by both. What did the researchers do and find? Our study identified the birth weights at each gestational week at which the risk of serious neonatal morbidity and neonatal mortality (SNMM) was lowest and elevated to varying degrees, and showed that the corresponding Intergrowth and WHO centiles were right-shifted compared with population centiles. Outcome-based birth weight and centile cutoffs performed poorly for predicting serious neonatal morbidity and neonatal mortality (SNMM) at the individual level. The population attributable fractions associated with these Outcome-based birth weight and centile cutoffs cut-offs were also small. The birth weight distributions of live births and SNMM cases (at each gestational week) overlapped substantially, showing that birth weight-for-gestational age in isolation cannot serve as an accurate predictor of adverse neonatal outcomes, irrespective of the cut-off used to identify SGA and LGA infants. What do these findings mean? Using birth weight-for-gestational age cutoffs to identify SGA, AGA and LGA infants does not add significantly to individual- or population-level prediction of adverse neonatal outcomes. Birth weight-for-gestational age centiles are best suited for use in multivariable prognostic functions, in conjunction with other prognostic indicators of adverse perinatal 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 | 0.005 | 0.018 |
| 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 | 0.001 |
| 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".