Estimating gestational age at birth: a population-based derivation-validation study
Bibliographic record
Abstract
INTRODUCTION: Information on newborn gestational age (GA) is essential in research on perinatal and infant health, but it is not always available from administrative databases. We developed and validated a GA prediction model for singleton births for use in epidemiological studies. METHODS: Derivation of estimated GA was calculated based on 130 328 newborn infants born in Ontario hospitals between 2007 and 2009, using linear regression analysis, with several infant and maternal characteristics as the predictor (independent) variables. The model was validated in a separate sample of 130 329 newborns. RESULTS: The discriminative ability of the linear model based on infant birth weight and sex was reasonably approximate for infants born before the 37th week of gestation (r2 = 0.67; 95% CI: 0.65-0.68), but not for term births (37-42 weeks; r2 = 0.12; 95% CI: 0.12-0.13). Adding other infant and maternal characteristics did not improve the model discrimination. CONCLUSION: Newborn gestational age before 37 weeks can be reasonably approximated using locally available data on birth weight and 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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".