Potentially Inappropriate Medication Use Among Older Adults with Cognitive Impairment and Dementia Attending Primary Care-Based Memory Clinics
Bibliographic record
Abstract
Potentially inappropriate medications (PIMs) increase the risk of adverse drug reactions, hospitalizations, and worsened health outcomes in older adults, particularly those with cognitive impairment (CI) or dementia. This study was designed to compare the Beers Criteria® 2023 and the Screening Tool of Older Persons’ Potentially Inappropriate Prescriptions (STOPP) Criteria 2023 to determine which identifies a higher prevalence of PIMs in older adults with CI or dementia attending primary care-based memory clinics. PIMs were identified with the use of the updated Beers Criteria® 2023 and STOPP Criteria 2023, from electronic medical records of study participants from January to August 2023. The study identified PIMs and analyzed associated risk factors using bivariate logistic regression. Of 44 older adults, 47.7% (n = 21) were detected with one PIM based on Beers Criteria® 2023, and 27.2% (n = 12) were identified with at least one PIM using STOPP criteria. Using the updated Beers Criteria® 2023 and STOPP Criteria 2023, the study identified 50 PIMs (averaging 0.9 PIMs per participant) based on Beers Criteria® and 31 PIMs (averaging 0.6 PIMs per participant) based on STOPP Criteria, respectively. Bivariate logistic regression revealed a significant association between having nine or more comorbidities and PIMs according to Beers Criteria® (odds ratio (OR) = 8.4, 95% confidence interval (CIn) = 1.27–55.39, p = 0.027). This study highlights the high prevalence of PIMs among older adults with CI or dementia, emphasizing the need for regular medication reviews. Implementing both criteria can enhance medication management and improve patient safety in this vulnerable population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".