Antidepressant Use and Cognitive Functioning in Older Medical Patients With Major or Minor Depression
Bibliographic record
Abstract
OBJECTIVE: The long-term cognitive effect of antidepressant medications in older persons is not well understood, especially in those with minor depression and complex medical conditions. The objective of this study is to examine this relationship in older medical patients with different depression diagnoses. METHODS: 281 medical inpatients aged 65 years and older from 2 acute care hospitals in Montreal, Canada, were diagnosed as with major or minor depression or without depression according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria. They were followed up with the Mini-Mental State Examination for cognitive function and the Hamilton Depression Rating Scale for depressive symptoms at baseline and 3, 6, and 12 months after discharge. Antidepressant medication was ascertained from a provincial prescription database and quantified as cumulative exposures over each follow-up interval. RESULTS: During the 12-month follow-up period, 1027 antidepressant prescriptions were filled. The most frequently prescribed antidepressant agents were citalopram (0.81 prescriptions per person), sertraline (0.76), and paroxetine (0.66). Antidepressant use was not associated with cognitive changes among patients with major depression or without depression but was associated with an increased Mini-Mental State Examination score in patients with minor depression (1.4 points; 95% confidence interval, 0.1-2.6), independent of change in the severity of depression symptoms, concomitant benzodiazepine or psychotropic drug use, and other potentially important confounders. CONCLUSIONS: In this cohort of older medical patients, antidepressant use for 12 months did not lead to significant cognitive impairment. The small cognitive improvement among minor depression associated with antidepressant use deserves further investigation.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".