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 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 | 0.001 | 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 | 0.000 |
| 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".