Comparison of Gestational Weight Gain z‐Scores and Traditional Weight Gain Measures in Relation to Perinatal Outcomes
Bibliographic record
Abstract
BACKGROUND: Conventional measures of gestational weight gain (GWG) are correlated with pregnancy duration, and may induce bias to studies of GWG and perinatal outcomes. A maternal weight-gain-for-gestational-age z-score chart is a new tool that allows total GWG to be classified as a standardised z-score that is independent of gestational duration. Our objective was to compare associations with perinatal outcomes when GWG was assessed using gestational age-standardised z-scores and conventional GWG measures. METHODS: We studied normal-weight (n=522 120) and overweight (n=237 923) women who delivered liveborn, singleton infants in Pennsylvania, 2003-11. GWG was expressed using gestational age-standardised z-scores and three traditional measures: total GWG (kg), rate of GWG (kg per week of gestation), and the GWG adequacy ratio (observed GWG/GWG recommended by the Institute of Medicine). Log-binomial regression models were used to assess associations between GWG and preterm birth, and small- and large-for-gestational-age births, while adjusting for race/ethnicity, education, smoking, and other confounders. RESULTS: The association between GWG z-score and preterm birth was approximately U-shaped. The risk of preterm birth associated with weight gain <10th percentile of each measure was substantially overestimated when GWG was classified using total kilogram and was moderately overestimated using rate of GWG or GWG adequacy ratio. All GWG measures had similar associations with small- or large-for-gestational-age birth. CONCLUSIONS: Our findings suggest that studies of gestational age-dependent outcomes misspecify associations if total GWG, rate of GWG, or GWG adequacy ratio are used. The potential for gestational age-related bias can be eliminated by using z-score charts to classify total GWG.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".