The Missing Data Problem in Birth Weight Percentiles and Thresholds for "Small-for-Gestational-Age"
Bibliographic record
Abstract
Weight-for-gestational-age charts and definitions of "small-for-gestational-age" based on the distribution of livebirths at a given gestational age have conventionally been used to identify infants whose fetal growth is poor. However, references based on the weights of only livebirths have serious shortcomings at preterm ages due to missing data on the weights of fetuses still in utero, and these missing data introduce considerable bias to etiologic studies of fetal growth restriction. Application of standard epidemiologic approaches for missing data is needed to help produce perinatal weight percentiles that provide unbiased assessment of fetal growth and risks of small-for-gestational-age.
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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 | 0.235 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".