Prevalence of central nervous system-active polypharmacy in a cohort of older adults in Argentina
Bibliographic record
Abstract
BACKGROUND: Central nervous system (CNS)-active polypharmacy is frequent and potentially harmful in older patients. Data on its burden outside the USA and European countries remain limited. AIMS: To estimate the period prevalence of and factors associated with out-of-hospital CNS-active polypharmacy in older adults. METHOD: We used data from a cohort of out-patients aged ≥60 years affiliated to the Hospital Italiano de Buenos Aires' health maintenance organisation on 1 January 2021. A CNS-active polypharmacy event was defined as the concurrent exposure to ≥3 CNS-active medications (i.e. antidepressants, anti-epileptics, antipsychotics, benzodiazepines, Z-drugs and opioids) through filled out-of-hospital prescriptions. We calculated the period prevalence of CNS-active polypharmacy for 2021. We identified factors associated with CNS-active polypharmacy using a multivariable logistic regression model to estimate odds ratios and 95% confidence intervals (CI). RESULTS: We included 63 857 patients. Pre-existing mental health diagnoses included anxiety (21%), depressive (14%) and sleep (11%) disorders. CNS-active polypharmacy occurred in 4535 patients, for a period prevalence of 7.1% (95% CI: 6.9-7.3%). The combination of an antidepressant, an antipsychotic and a benzodiazepine accounted for 21% of the CNS-active polypharmacy events. Frontotemporal dementia (odds ratio: 14.67; 95% CI: 4.47-48.20), schizophrenia (odds ratio: 7.93; 95% CI: 4.64-13.56), bipolar disorder (odds ratio: 7.20; 95% CI: 5.45-9.50) and depressive disorder (odds ratio: 3.50; 95% CI: 3.26-3.75) were associated with CNS-active polypharmacy. CONCLUSIONS: One in 14 adults aged 60 years and older presented out-of-hospital CNS-active polypharmacy. Future studies should evaluate measures to reduce CNS-active medication use in this population.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".