Pregnancy Weight Gain by Gestational Age in Women with Uncomplicated Dichorionic Twin Pregnancies
Bibliographic record
Abstract
BACKGROUND: Twin pregnancies are at increased risk for adverse outcomes and are associated with greater gestational weight gain compared to singleton pregnancies. Studies that disentangle the relationship between gestational duration, weight gain and adverse outcomes are needed to inform weight gain guidelines. We created charts of the mean, standard deviation and select percentiles of maternal weight gain-for-gestational age in twin pregnancies and compared them to singleton curves. METHODS: We abstracted serial prenatal weight measurements of women delivering uncomplicated twin pregnancies at Magee-Womens Hospital (Pittsburgh, PA, 1998-2013) and merged them with the hospital's perinatal database. Hierarchical linear regression was used to express pregnancy weight gain as a smoothed function of gestational age according to pre-pregnancy BMI category. Charts of week- and day-specific values for the mean, standard deviation, and percentiles of maternal weight gain were created. RESULTS: Prenatal weight measurements (median: 11 [interquartile range: 9, 13] per woman) were available for 1109 women (573 normal weight, 287 overweight, and 249 obese). The slope of weight gain was most pronounced in normal weight women and flattened with increasing pre-pregnancy BMI (e.g. 50th percentiles of 6.8, 5.7, and 3.6 kg at 20 weeks and 19.8, 18.1, and 14.4 at 37 weeks in normal weight, overweight, and obese women, respectively). Weight gain patterns in twins diverged from singletons after 17-19 weeks. CONCLUSIONS: Our charts provide a tool for the classification of maternal weight gain in twin pregnancies. Future work is needed to identify the range of weight gain associated with optimal pregnancy health 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 | 0.001 | 0.007 |
| 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 | 0.001 |
| 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".