Gestational weight loss and fetal growth in uncomplicated pregnancies among women with obesity: a population-based retrospective cohort study
Bibliographic record
Abstract
Abstract Objective The impact of gestational weight loss (GWL) on fetal growth among women with obesity remains unclear. This study aimed to examine the association between weight loss during pregnancy among women with body mass index (BMI) ≥ 30 kg/m 2 and the risk of small-for-gestational-age (SGA) and large-for-gestational-age (LGA) neonates. Methods We conducted a retrospective, population-based cohort study of women with pre-pregnancy obesity that resulted in a singleton live birth in 2012-2017, using birth registry data in Ontario, Canada. Women with pregnancy complications or health conditions which could cause weight loss were excluded. GWL is defined as negative gestational weight change (≤0 kg). The association between GWL and fetal growth was estimated using generalized estimating equation models and restricted cubic spline regression analysis. Stratified analysis was conducted by obesity class (I:30–34.9 kg/m 2 , II:35–39.9 kg/m 2 , and III + : ≥40 kg/m 2 ). Results Of the 52,153 eligible women who entered pregnancy with a BMI ≥ 30 kg/m 2 , 5.3% had GWL. Compared to adequate gestational weight gain, GWL was associated with an increased risk of SGA neonates (aRR:1.45, 95% CI: 1.30–1.60) and a decreased risk of LGA neonates (aRR: 0.81, 95% CI:0.73–0.93). Non-linear L-shaped associations were observed between gestational weight change and SGA neonates, with an increased risk of SGA observed with increased GWL. On the contrary, non-linear S-shaped associations were observed between gestational weight change and LGA neonates, with a decreased risk of LGA observed with increased GWL. Similar findings were observed from the stratified analysis by obesity class. Conclusion These findings highlight that GWL in women with obesity may increase the risk of SGA neonates but reduce the risk of LGA neonates. Recommendations of GWL for women with obesity should be interpreted with caution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".