Polypharmacy Thresholds that Best Predict Emergency Room Visits and Mortality in Older Adults: A Population-Based Study in Québec, Canada
Bibliographic record
Abstract
BackgroundPolypharmacy is prevalent among older adults. Identifying informative medication thresholds could enhance clinical decision-making and healthcare planning.ObjectivesTo measure the predictive capacity of medication count and polypharmacy thresholds on (1) frequent emergency visits and (2) mortality, in community-dwelling older adults in Quebec, Canada; To measure the predictive capacity of inappropriateness criteria (potentially inappropriate medications, anticholinergic burden level, and drug-drug interactions) on these same outcomes.MethodsWe conducted a population-based study using the Quebec Integrated Chronic Disease Surveillance System. Using multivariable logistic regression models, we assessed the predictive capacity [area under the curve (AUC)] of medication use (medication count, polypharmacy thresholds, number of inappropriateness criteria) on frequent emergency visits (≥3) and mortality from April 2, 2022, to March 31, 2023. Findings were interpreted from two perspectives: clinical (with models including individuals' clinical characteristics, such as chronic diseases) and public health (with models excluding clinical characteristics to mimic polypharmacy measures usually used in surveillance).ResultsMedication count remained the most informative predictor in the clinical perspective analyses, with AUC = 0.739 for frequent emergency visits and AUC = 0.804 for one-year mortality, while medication thresholds lacked discrimination. Public health perspective analyses resulted in a bell-shaped predictive pattern peaking at eight medications for both frequent emergency visits (AUC = 0.690) and one-year mortality (AUC = 0.764). Inappropriateness criteria did not outperform medication count or polypharmacy thresholds.ConclusionFrom a clinical perspective, medication count is more informative than polypharmacy thresholds. From a public health perspective, a threshold of eight medications may serve as a useful polypharmacy measure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".