An Outcome-based Approach for the Creation of Fetal Growth Standards: Do Singletons and Twins Need Separate Standards?
Bibliographic record
Abstract
Contemporary fetal growth standards are created by using theoretical properties (percentiles) of birth weight (for gestational age) distributions. The authors used a clinically relevant, outcome-based methodology to determine if separate fetal growth standards are required for singletons and twins. All singleton and twin livebirths between 36 and 42 weeks' gestation in the United States (1995-2002) were included, after exclusions for missing information and other factors (n = 17,811,922). A birth weight range was identified, at each gestational age, over which serious neonatal morbidity and neonatal mortality rates were lowest. Among singleton males at 40 weeks, serious neonatal morbidity/mortality rates were lowest between 3,012 g (95% confidence interval (CI): 3,008, 3,018) and 3,978 g (95% CI: 3,976, 3,980). The low end of this optimal birth weight range for females was 37 g (95% CI: 21, 53) less. The low optimal birth weight was 152 g (95% CI: 121, 183) less for twins compared with singletons. No differences were observed in low optimal birth weight by period (1999-2002 vs. 1995-1998), but small differences were observed for maternal education, race, parity, age, and smoking status. Patterns of birth weight-specific serious neonatal morbidity/neonatal mortality support the need for plurality-specific fetal growth standards.
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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 | 0.070 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".