Customised birthweight percentiles: does adjusting for maternal characteristics matter?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine whether the improved prediction of risk for perinatal mortality obtained with the use of a customised birthweight standard can also be obtained with the use of a non-customised but intrauterine-based standard. DESIGN: Population-based cohort study. SETTING: Sweden. POPULATION: Births in the Swedish Medical Birth Register between 1992 and 2001 (n = 782 303) with complete data on birthweight, gestational age, sex, maternal age, pre-pregnancy body mass index, height, parity, and ethnicity. METHODS: We calculated the relative risks (RRs) of stillbirth and early neonatal mortality among small-for-gestational-age (SGA) births as established by (1) a customised standard, (2) a population standard based on birthweights, and (3) a population standard based on a best estimate of intrauterine weights. MAIN OUTCOME MEASURES: Stillbirth and early neonatal mortality (<7 days). RESULTS: The RRs of stillbirth and early neonatal mortality among SGA births as classified by the intrauterine standard were similar to those among SGA births as classified by the customised standard and much higher than those among SGA births as classified by the birthweight standard. CONCLUSIONS: A non-customised but intrauterine-based standard has a similar ability to predict risk for stillbirth and early neonatal mortality as a customised birthweight standard. The process of customising population weight-for-gestational-age standards to account for maternal characteristics does little to improve prediction of perinatal 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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".