Increased Prevalence of Obesity and Glucose Intolerance in Youth Treated with Second—Generation Antipsychotic Medications
Bibliographic record
Abstract
OBJECTIVE: To compare the rates of obesity, impaired fasting glucose (IFG), and type 2 diabetes between second-generation antipsychotic (SGA)-treated and -naive youth. METHODS: A retrospective chart review was conducted for all child and adolescent psychiatry emergency admissions over 2.5 years. Data collected included age, sex, psychiatric diagnosis, medications, height, weight, fasting glucose, and lipid profile. Body mass index (BMI) was standardized for age and sex and converted to a z score. Overweight was defined as a BMI between the 85th and 95th percentile and obese as a BMI at the 95th percentile or greater for age and sex. The 2007 American Diabetes Association criteria for IFG and type 2 diabetes were used. RESULTS: Among the 432 admissions, 167 (39%) had both height and weight measured, and 145 (34%) had fasting glucose measured. The mean zBMI was higher in the SGA-treated (n = 68), compared with the SGA-naive group (n = 99) (mean difference 0.81; 95% CI 0.46 to 1.16). In the SGA-treated group, 31% were obese and 26% were overweight, compared with 15% and 8%, respectively, in the SGA-naive group (P < 0.01). In the SGA-treated group (n = 65), 21.5% had IFG or type 2 diabetes, compared with 7.5% in the SGA-naive group (n = 80) (P = 0.01). CONCLUSIONS: Youth treated with SGAs have significantly higher rates of obesity and glucose intolerance than SGA-naive youth. These data emphasize the need for consistent metabolic monitoring of youth with psychiatric disorders who are prescribed SGAs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".