The association of psychotropic medication use with the cognitive, functional, and neuropsychiatric trajectory of Alzheimer's disease
Bibliographic record
Abstract
OBJECTIVE: The use of psychotropic medications in Alzheimer's disease (AD) has been associated with both deleterious and potentially beneficial outcomes. We examined the longitudinal association of psychotropic medication use with cognitive, functional, and neuropsychiatric symptom (NPS) trajectories among community-ascertained incident AD cases from the Cache County Dementia Progression Study. METHODS: A total of 230 participants were followed for a mean of 3.7 years. Persistency index (PI) was calculated for all antidepressants, selective serotonin reuptake inhibitors (SSRIs), antipsychotics (atypical and typical), and benzodiazepines as the proportion of observed time of medication exposure. Mixed-effects models were used to examine the association between PI for each medication class and Mini-Mental State Exam (MMSE), Clinical Dementia Rating Sum of Boxes (CDR-Sum), and Neuropsychiatric Inventory - Total (NPI-Total) trajectories, controlling for appropriate demographic and clinical covariates. RESULTS: At baseline, psychotropic medication use was associated with greater severity of dementia and poorer medical status. Higher PI for all medication classes was associated with a more rapid decline in MMSE. For antidepressant, SSRI, benzodiazepine, and typical antipsychotic use, a higher PI was associated with a more rapid increase in CDR-Sum. For SSRIs, antipsychotics, and typical antipsychotics, a higher PI was associated with more rapid increase in NPI-Total. CONCLUSIONS: Psychotropic medication use was associated with more rapid cognitive and functional decline in AD, and not with improved NPS. Clinicians may tend to prescribe psychotropic medications to AD patients at risk of poorer outcomes, but one cannot rule out the possibility of poorer outcomes being caused by psychotropic medications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".