Large Maternal Weight Loss From Obesity Surgery Prevents Transmission of Obesity to Children Who Were Followed for 2 to 18 Years
Bibliographic record
Abstract
OBJECTIVE: Our aim was to compare the prevalence of obesity in 172 children who were aged 2 to 18 years and born to 113 obese mothers (BMI: 31 +/- 9 kg/m2) with substantial weight loss after biliopancreatic bypass surgery with 45 same-age siblings who were born before maternal surgery (mothers' BMI: 48 +/- 8 kg/m2) and with current population standards. METHODS: In this case series, with >88% follow-up in a tertiary referral center, cross sectional office chart and telephone data on childhood and adolescent weights were transformed to z scores. RESULTS: After maternal surgery, the prevalence of obesity in the offspring decreased by 52% and severe obesity by 45.1%, with no increase in the prevalence of underweight. The z score reduction in obesity was gender specific, with boys reducing from 1.4 +/- 1.3 before to 0.57 +/- 1.7 after maternal surgery, corrected for birth order. The difference was not significant in girls (0.8 +/- 1.3 vs 0.8 +/- 1.2). Among children of both genders who were aged 6 to 18 years of age and born after maternal surgery, the prevalence of overweight was reduced to population levels. CONCLUSIONS: Contrary to outcomes after intrauterine under- and overnutrition, the prevalence of overweight and obesity in children of mothers with large voluntary postsurgical weight loss was similar to that in the general population, with no increase in underweight. The results demonstrate the importance of potentially modifiable epigenetic factors in the cause of obesity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".