Pregnancy weight gain charts for obese and overweight women
Bibliographic record
Abstract
OBJECTIVE: Reference charts for classifying and monitoring pregnancy weight gain in severely obese women do not exist. The goal was to construct pregnancy weight-gain-for-gestational-age z-score charts for overweight and obese mothers, stratified by severity of obesity. METHODS: Serial weight gain measurements were abstracted from 1047, 1202, 1267, and 730 overweight, class I, II, and III obese women, respectively, delivering uncomplicated term pregnancies at Magee-Womens Hospital in Pittsburgh, PA. Multi-level linear regression models were used to express serial weight gain measurements as a function of gestational age. RESULTS: There were a median [interquartile range] of 11 [9-12] and 11 [9-13] serial weight measurements for overweight and obese (class I, II, and III) women, respectively. The rate of weight gain was minimal until 15-20 weeks and then increased in a slow, linear manner until term. The slope of weight gain flattened as pre-pregnancy BMI increased. Charts were created describing the mean, standard deviation, and select percentiles of weight gain in class I, II, and III obese and overweight pregnancies. CONCLUSIONS: These charts are an innovative tool for studying the association between gestational weight gain and adverse pregnancy outcomes.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".