Medication Use by Older Adults with Frailty: A Scoping Review
Bibliographic record
Abstract
Frailty among older adults heightens their risk of negative health outcomes, and medication use plays a major role in this increased vulnerability. Various aspects of medication use elevate the risk of poor outcomes in individuals with frailty. The current scoping review was designed to explore medication use in older adults with frailty in primary care, focusing on the prevalence of potentially inappropriate medications (PIMs), polypharmacy, medication adherence, and their role in contributing to adverse drug events. This scoping review was conducted using the Arksey and O'Malley, supplemented by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews (PRISMA-ScR) guidelines. A search of the literature was conducted from inception to November 2023 in Ovid EMBASE, PubMed (MEDLINE), Scopus, EBSCOhost CINAHL, and Ovid International Pharmaceutical Abstracts. Studies which met the eligibility criteria included older adults with frailty (≥65 years) living at home, defined frailty criteria, and assessment of medication use. Out of the 4726 studies screened, 223 were included, conducted across 39 countries. Frailty prevalence varied widely from 0.9% to 89.2%. Polypharmacy (5-9 medications) and hyper-polypharmacy (≥10 medications) were notably more common among individuals with frailty, with polypharmacy rates ranging from 1.3% to 96.4%. Twelve studies reported PIM prevalence among individuals with varying levels of frailty, ranging from 2.4% to 95.9%. This scoping review highlights the challenges and complexities involved in understanding the relationship between medication use and frailty in older adults.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".