Printed in U.S.A. Implausible Birth Weight for Gestational Age Joseph et al. Implausible Birth Weight for Gestational Age
Bibliographic record
Abstract
Various rules have been proposed to identify and exclude live births with implausible values of birth weight for gestational age from large perinatal data sets. The authors carried out a preliminary evaluation of common rules by examining the frequency and nature of rule-based exclusions among live births in Canada (excluding Ontario) between 1992 and 1994. There were 625 (0.09%), 133 (0.02%), 170 (0.02%), and 2,858 (0.40%) live births identified for exclusion by a median birth weight for gestational age ±4 standard deviations (SD) rule, a ±5 SD rule, a rule based on expert clinical opinion, and a modification of Tukey’s rule, respectively. The birth weight and gestational age distribution of the exclusions depended on the particular rule used; for example, 12.1 % and 0.3 % of live births of ≥4,500 g were excluded under Tukey’s rule and the rule based on expert opinion, respectively. Infant mortality rates among those excluded were 8–13 times higher than among all live births. Current rules for identifying implausible birth weight for gestational age tend to flag live births at high risk for infant death. Such rules may erroneously attenuate temporal trends in important perinatal outcomes. Am J Epidemiol 2001;153:110–13. birth weight; databases; epidemiologic studies; gestational age; infant; mortality
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.476 | 0.305 |
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".