Gestational Weight Gain‐for‐Gestational Age <i>Z</i>‐Score Charts Applied across U.S. Populations
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Gestational weight gain may be a modifiable contributor to infant health outcomes, but the effect of gestational duration on gestational weight gain has limited the identification of optimal weight gain ranges. Recently developed z-score and percentile charts can be used to classify gestational weight gain independent of gestational duration. However, racial/ethnic variation in gestational weight gain and the possibility that optimal weight gain differs among racial/ethnic groups could affect generalizability of the z-score charts. The objectives of this study were (1) to apply the weight gain z-score charts in two different U.S. populations as an assessment of generalisability and (2) to determine whether race/ethnicity modifies the weight gain range associated with minimal risk of preterm birth. METHODS: The study sample included over 4 million live, singleton births in California (2007-2012) and Pennsylvania (2003-2013). We implemented a noninferiority margin approach in stratified subgroups to determine weight gain ranges for which the adjusted predicted marginal risk of preterm birth (gestation <37 weeks) was within 1 or 2 percentage points of the lowest observed risk. RESULTS: There were minimal differences in the optimal ranges of gestational weight gain between California and Pennsylvania births, and among several racial/ethnic groups in California. The optimal ranges decreased as severity of prepregnancy obesity increased in all groups. CONCLUSIONS: The findings support the use of weight gain z-score charts for studying gestational age-dependent outcomes in diverse U.S. populations and do not support weight gain recommendations tailored to race/ethnicity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it