Impact of Psychoactive Drug Use on Developing Obesity among Children and Adolescents with Autism Spectrum Diagnosis: A Nested Case–Control Study
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Bibliographic record
Abstract
BACKGROUND: Obesity in children on the autism spectrum (AS) is becoming a significant health concern. The purpose of this study was to identify the predictors of obesity in a cohort of AS youth and to assess the impact of psychoactive medication use while exploring the second-generation antipsychotics (SGAs) dose-response curve. STUDY DESIGN: A nested case-control study was conducted using Quebec public administrative databases. Subjects with AS <18 years [≥2 diagnoses International Classification of Diseases: 9th revision (ICD-9): 299.X] were identified (January 1993 to May 2011). Cases were defined as subjects with an obesity diagnosis (ICD-9: 278.X) during the coverage period and matched to 10 controls for age, gender, and follow-up duration. Potential risk factors for obesity (sociodemographic characteristics, other neuropsychiatric conditions, and psychoactive drug use) were evaluated and analyzed using conditional logistic regression. RESULTS: From a cohort of 5369 AS subjects, we identified 135 obesity cases. Among the different risk factors, only SGAs [rate ratio (RR): 1.04, 95% confidence interval (CI): 1.01-1.07] increased the probability of obesity in multivariate analysis. Exposure for ≥12 months increased significantly the likelihood of obesity (RR: 2.01, 95% CI: 1.18-3.42). Higher risk was observed with chlorpromazine-equivalent daily doses ≥100 mg (RR: 2.20, 95% CI: 1.00-4.84). Among SGA users, concomitant antidepressants (per 30-day exposure) slightly increased the probability (RR: 1.08, 95% CI: 1.01-1.15). CONCLUSIONS: Longer and higher SGA exposure increased the risk of obesity, which has to be considered in relation to the paucity of evidence supporting long-term psychoactive medication use in AS children. Results highlight the need to promote optimal use and interventions to mitigate metabolic side effects of SGAs in this population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it