Gestational Weight Gain‐for‐Gestational Age <i>Z</i>‐Score Charts Applied across U.S. Populations
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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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".