A Sibling Case-Control Study of Maternal Prenatal Body Mass Index as a Risk Factor For Autism Spectrum Disorder
Bibliographic record
Abstract
The prevalence of autism spectrum disorder (ASD) is estimated to be one in every 150 births. While both genetic and postpartum environmental exposure have been linked to ASD, prenatal maternal weight has not been investigated. The objective of the study is to assess whether overweight or obesity at pregnancy is an important risk factor for the diagnosis of ASD in offspring. A case-control study was designed to answer this question using the public health ecosocial theory. The study population consisted of 70 mothers, who were recruited via the Internet using the viral expansion loop. Multiple logistic regression analysis was used to test the hypotheses. No significant difference in risk of ASD by level of body mass index (BMI) was found after adjusting for covariates. The odds ratio for obese women in comparison to normal or underweight women was 1.19, 95% CI [0.53, 2.66] after adjusting for covariates. Gaining the appropriate amount of weight during gestation, as determined by the Institute of Medicine, was not associated with ASD either, with the odds ratio at 0.67, 95% CI [0.31, 1.48]. The results indicate that BMI category at pregnancy and gestational weight gain were not risk factors for autism in children. The implications for positive social change include a better understanding of maternal prenatal BMI as a risk factor for autism spectrum disorder. Appropriate health information provided to mothers prenatally could result in improved birth 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".