Assessing the Value of Customized Birth Weight Percentiles
Bibliographic record
Abstract
Customized birth weight percentiles are weight-for-gestational-age percentiles that account for the influence of maternal characteristics on fetal growth. Although intuitively appealing, the incremental value they provide in the identification of intrauterine growth restriction (IUGR) over conventional birth weight percentiles is controversial. The objective of this study was to assess the value of customized birth weight percentiles in a simulated cohort of 100,000 infants aged 37 weeks whose IUGR status was known. A cohort of infants with a range of healthy birth weights was first simulated on the basis of the distributions of maternal/fetal characteristics observed in births at the Royal Victoria Hospital in Montreal, Canada, between 2000 and 2006. The occurrence of IUGR was re-created by reducing the observed birth weights of a small percentage of these infants. The value of customized percentiles was assessed by calculating true and false positive rates. Customizing birth weight percentiles for maternal characteristics added very little information to the identification of IUGR beyond that obtained from conventional weight-for-gestational-age percentiles (true positive rates of 61.8% and 61.1%, respectively, and false positive rates of 7.9% and 8.5%, respectively). For the process of customization to be worthwhile, maternal characteristics in the customization model were shown through simulation to require an unrealistically strong association with birth weight.
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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 | 0.025 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".