Racial/ethnic variations in gestational weight gain: a population-based study in Ontario
Bibliographic record
Abstract
OBJECTIVE: To explore inadequate and excessive gestational weight gain (GWG) among pregnant women of different racial/ethnic backgrounds in Ontario, Canada. METHODS: A population-based retrospective cohort study was conducted among women who had prenatal screening and had a singleton birth in an Ontario hospital between April 2016 and March 2017. We estimated adjusted risk ratios (aRR) of racial/ethnic differences for inadequate or excessive GWG using multinomial logistic regression models. Interaction effects were examined to determine whether racial/ethnic difference in GWG varied by pre-pregnancy body mass index (BMI). RESULTS: Among 74,424 women, the prevalence of inadequate GWG in White, Asian, and Black women was 15.7%, 25.8%, and 25.0%, and excessive GWG was 62.8%, 45.5%, and 54.7%, respectively. There were significant interaction effects between race/ethnicity and pre-pregnancy BMI for inadequate GWG (Wald p < 0.01) and excessive GWG (Wald p < 0.01). Compared with White women, Asian women had higher risk of inadequate GWG and lower risk of excessive GWG in all weight classes, and Black women had higher risk of inadequate GWG and lower risk of excessive GWG if their BMI was normal, overweight, or obese. CONCLUSION: Variations in unhealthy GWG by pre-pregnancy weight classes among Ontario White, Asian and Black women were observed. Individualized counseling regarding appropriate GWG is universally recommended. Additional consideration of racial/ethnic variations by maternal weight classes may help to promote healthy GWG in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".