Should Gestational Weight Gain Recommendations be Tailored by Maternal Characteristics?
Bibliographic record
Abstract
The authors tested whether the relation between gestational weight gain (GWG) and 5 adverse pregnancy outcomes (small-for-gestational-age (SGA) birth, large-for-gestational-age (LGA) birth, spontaneous preterm birth, indicated preterm birth, and unplanned cesarean delivery) differed according to maternal race/ethnicity, smoking, parity, age, and/or height. They also evaluated whether GWG guidelines should be modified for special populations by studying GWG and risk of at least 1 adverse outcome within different subgroups. Data came from a cohort of 23,362 normal-weight mothers who delivered singletons at Magee-Womens Hospital in Pittsburgh, Pennsylvania (2003-2008). Adequacy of GWG was defined as observed GWG divided by recommended GWG. The synergy analysis found that the combination of smoking, black race/ethnicity, primiparity, or short height with poor GWG was associated with an excess risk of SGA birth, while high GWG combined with each of these characteristics diminished risk of LGA birth in comparison with the same GWG among the women's counterparts. Nevertheless, there were no significant or meaningful differences in the risk of at least 1 adverse outcome between the GWG recommended by the Institute of Medicine in 2009 and the GWG that minimized risk of the composite outcome. These findings do not support the tailoring of GWG guidelines on the basis of a mother's smoking status, race/ethnicity, parity, age, or height among normal-weight women.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 teacher head, 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".