Greater early and mid‐pregnancy gestational weight gains are associated with excess adiposity in mid‐childhood
Bibliographic record
Abstract
OBJECTIVE: It is unclear how specific periods of gestational weight gain (GWG) during pregnancy relate to childhood adiposity. The goal of this study was to assess the differential impact of GWG timing on childhood body composition. METHODS: In 979 mother-child pairs from the pre-birth Project Viva cohort, trimester-specific GWG was calculated using clinically recorded weights. Outcomes included body mass index (BMI) z-score, dual X-ray absorptiometry fat mass index (kg/m(2) ), and fat-free mass index (kg/m(2) ) in mid-childhood. Linear regression models were used to assess associations of each trimester's GWG (per 0.2 kg/week) with childhood outcomes, adjusted for maternal prepregnancy BMI, sociodemographic variables, lifestyle, and GWG in prior trimester(s). RESULTS: Mean (SD) first trimester GWG was 0.22 (0.22) kg/week, second trimester 0.49 (0.18) kg/week, and third trimester 0.47 (0.20) kg/week. Faster first trimester GWG was associated with higher BMI z-score (0.06 units [95% CI: 0.01-0.12] per 0.2 kg/week) and with higher adiposity according to all indices; associations were strongest in women with prepregnancy BMI >30 kg/m(2) . Faster second trimester GWG was associated with higher BMI z-score (0.11 [0.04-0.18]), fat mass (fat mass index = 0.16 [0.02-0.31] kg/m(2) ), and lean mass (fat-free mass index = 0.11 [0.01-0.22] kg/m(2) ). Third trimester GWG was not associated with childhood adiposity. CONCLUSIONS: These results reinforce the importance of addressing appropriate GWG in early pregnancy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".