Morbidity and mortality of women and men with intellectual and developmental disabilities newly initiating antipsychotic drugs
Bibliographic record
Abstract
BACKGROUND: While up to 45% of individuals with intellectual and developmental disabilities (IDD) have a comorbid psychiatric disorder, and antipsychotics are commonly prescribed, gender differences in the safety of antipsychotics have rarely been studied in this population. AIMS: To compare men and women with IDD on medical outcomes after antipsychotic initiation. METHOD: Our population-based study in Ontario, Canada, compared 1457 women and 1951 men with IDD newly initiating antipsychotic medication on risk for diabetes mellitus, hypertension, venous thromboembolism, myocardial infarction, stroke and death, with up to 4 years of follow-up. RESULTS: Women were older and more medically complex at baseline. Women had higher risks for venous thromboembolism (HR 1.72, 95% CI 1.15-2.59) and death (HR 1.46, 95% CI 1.02-2.10) in crude analyses; but only thromboembolism risk was greater for women after covariate adjustment (aHR 1.58, 95% CI 1.05-2.38). CONCLUSIONS: Gender should be considered in decision-making around antipsychotic medications for individuals with IDD. DECLARATION OF INTEREST: None. COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.
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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 | 0.000 | 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".