Association between number of medications and indicators of potentially inappropriate polypharmacy: a population-based cohort of older adults in Quebec, Canada
Bibliographic record
Abstract
Background: As the number of medications increases, the appropriateness of polypharmacy may become questionable due to the heightened risk of medication-related harm. Objectives: (1) To investigate the relationship between the number of current medications used by older adults and three indicators of potentially inappropriate polypharmacy: (a) the mean number of potentially inappropriate medications (PIMs), (b) the average count of drug-drug interactions, and (c) the anticholinergic burden; (2) To characterize the population-based burden of potentially inappropriate polypharmacy by calculating the proportion of individuals with these indicators. Design: We conducted a population-based observational study using the Quebec Integrated Chronic Disease Surveillance System. Methods: We included all individuals over 65 years insured by the public drug plan on April 1st, 2022. For each individual, we calculated the number of current medications and the number of (a) PIMs (Beers 2019), (b) drug-drug interactions (Beers 2019), and (c) anticholinergic burden (Anticholinergic Cognitive Burden (ACB) scale). The association between the number of medications and these indicators was quantified using linear regression. Prevalence with 99% confidence intervals (CIs) was calculated. Results: -trend <0.0001). Nearly half the population (45.5%; 99% CI: 45.5-45.5) had a regimen containing ⩾1 PIMs, ⩾1 drug-drug interaction, or an ACB ⩾3. Conclusion: The strong association between the increasing number of medications and reduced polypharmacy quality underscores the importance of medication count beyond therapeutic indications. With widespread medication use, many older adults face quality issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".