Predictors of Gestational Weight Gain Examined As a Continuous Outcome: A Prospective Analysis
Bibliographic record
Abstract
Background: Excess gestational weight gain (GWG) is common and adversely affects both mothers and offspring, including increasing the risk of maternal and childhood obesity. GWG is typically examined categorically, with women grouped into categories of those who gain above, within, and below guideline recommendations. Examining GWG as a continuous variable, rather than categorically, allows for a consideration of GWG at a finer level of detail, increasing precision. Methods: We collected exposure data among 970 pregnant women in early gestation using a standardized questionnaire in Ontario, Canada, from 2015 to 2017. Maternal weight and height were extracted from antenatal records. Continuous GWG was calculated using four methods: percentage of ideal weight gain, excess GWG, GWG adequacy ratio, and GWG z -score. We used the stepwise linear regression analyses to select variables associated with GWG. Results: We found that a common set of variables (parity, prepregnancy body mass index, planned pregnancy weight gain, smoking, pregnancy-related food cravings, and fast food intake) significantly predicted GWG in a manner consistent across the four GWG outcomes. Certain psychological factors, including the perception of families' and friends' attitudes toward the food cravings of pregnant women, emotion suppression, compensatory health beliefs coupled with eating unhealthy foods, frequent prepregnancy dietary restraint in carbohydrates, sugar, and meals, preferred prepregnancy body size image, agreeable and conscientious personalities, and depression, also were related with GWG. Conclusions: Our findings demonstrate that psychological factors play an important role in the magnitude of GWG, providing key avenues to inform interventions to support healthy weight gain in 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".