Diabetes mellitus onset in geriatric patients: does long‐term atypical antipsychotic exposure increase risk?
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) in common in adults using psychotropic medications. However, it remains largely unknown whether there is an additional risk of diabetes mellitus (DM) in elderly psychiatric outpatients, particularly those with long-term exposure to atypical antipsychotics (AP). METHODS: In this retrospective longitudinal study, 61 atypical AP-exposed and 64 atypical AP-unexposed geriatric psychiatric patients were compared to a group of 200 psychotropic-naïve controls. Our main composite outcome was diabetes incidence over a 4-year period, defined by fasting blood glucose ≥ 7.0 mmol/L or a new-onset oral hypoglycaemic or insulin prescription during the 4-year period. RESULTS: The 4-year incidence of DM did not differ significantly between groups: 12.3%, 6.7%, and 11.9% in the atypical AP-exposed, atypical AP-unexposed, and control groups, respectively (χ(2) = 1.40, P = 0.50). Depression and antidepressant, cholinesterase inhibitor, and valproate use were independently associated with increases in fasting glucose. However, hyperglycaemia and hypoglycaemic prescriptions were not more common in geriatric psychiatric patients. CONCLUSIONS: DM does not appear to be more common in geriatric psychiatric patients than similarly aged controls and is not more common in atypical AP users. However, depression and antidepressant, cholinesterase inhibitor, and valproate use may increase fasting glucose levels, and the clinical significance of this warrants further investigation. Nonetheless, given the rates of untreated and undertreated fasting hyperglycaemia in both our geriatric psychiatric and control samples (>10% of all patients), we recommend regular screening for DM in these populations.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".