Weight gain patterns among pregnancies with obesity and small‐ and large‐for‐gestational‐age births
Bibliographic record
Abstract
OBJECTIVE: ) (Magee-Womens Hospital, Pittsburgh, Pennsylvania, 1998-2011). METHODS: First-trimester GWG was categorized as being below (<0.2 kg), within (0.2-2.0 kg), or above (>2.0 kg) the Institute of Medicine recommendations. For second- and third-trimester GWG, four linear trajectories were derived: approximating maintenance (slope -0.05 ± 0.03 kg/wk), approximating the recommendations (0.27 ± 0.01 kg/wk; reference), higher than the recommendations (0.54 ± 0.01 kg/wk), and highest among those above the recommendations (0.91 ± 0.02 kg/wk). RESULTS: For classes I, II, and III, respectively, there were 1290, 1247, and 1198 pregnancies in the subcohort; 262, 171, and 123 SGA cases; and 353, 286, and 257 LGA cases. First-trimester GWG was not associated with SGA/LGA births. Second- and third-trimester weight maintenance was associated with potentially lower LGA risk (risk ratio [RR]: 0.80; 95% confidence interval [CI]: 0.55-1.1) but not higher SGA risk (RR: 0.98; 95% CI: 0.64-1.5) for class III. In addition, some sensitivity analyses supported no increased SGA risk with second- and third-trimester weight maintenance for classes I and II. CONCLUSIONS: Second- and third-trimester weight maintenance may be associated with more optimal birth weight for gestational age. However, how this could be achieved (e.g., through diet and exercise interventions) is unclear, given the observational design of our study.
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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 | 0.001 | 0.001 |
| 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 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".