Addressing Metabolic Comorbidity in Individuals With Intellectual and Developmental Disability on Antipsychotics
Bibliographic record
Abstract
PURPOSE/BACKGROUND: Individuals with intellectual and developmental disabilities (IDDs) are at increased risk for serious metabolic comorbidities, which is further exacerbated by the high rate of antipsychotic use in this population. There is currently a lack of literature on effective treatment options for antipsychotic-induced weight gain and metabolic abnormalities in IDD. This case series reports on the clinical use of metformin in patients with IDD on antipsychotics. METHODS/PROCEDURES: We conducted a retrospective review of patients in a novel clinical service at the Centre for Addiction and Mental Health in Toronto, Ontario, Canada for adults with IDD experiencing antipsychotic-related weight gain and other metabolic aberrations. Charts were reviewed for weight and other metabolic outcome measures before and after commencing metformin treatment. FINDINGS/RESULTS: In 11 patients referred to this clinic, the mean weight loss while on metformin treatment was 11.1 kg, with over 50% of the sample achieving clinically meaningful weight loss of >7%. Additional adaptive changes were observed for fasting glucose, glycated hemoglobin, triglyceride, and high-density lipoprotein cholesterol levels. IMPLICATIONS/CONCLUSIONS: In line with its use in severe mental illness, metformin may be a safe, effective, and accessible treatment option for patients with IDD experiencing metabolic adverse effects of antipsychotic medication. Further research and randomized controlled trials are needed to examine the efficacy of metformin in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".