Potentially inappropriate medication use: the Beers’ Criteria used among older adults with depressive symptoms
Bibliographic record
Abstract
INTRODUCTION: The ageing population means prescribing for chronic illnesses in older people is expected to rise. Comorbidities and compromised organ function may complicate prescribing and increase medication-related risks. Comorbid depression in older people is highly prevalent and complicates medication prescribing decisions. AIM: To determine the prevalence of potentially inappropriate medication use in a community-dwelling population of older adults with depressive symptoms. METHODS: The medications of 191 community-dwelling older people selected because of depressive symptoms for a randomised trial were reviewed and assessed using the modified version of the Beers' Criteria. The association between inappropriate medication use and various population characteristics was assessed using Chi-square statistics and logistic regression analyses. RESULTS: The mean age was 81 (±4.3) years and 59% were women. The median number of medications used was 6 (range 1-21 medications). The most commonly prescribed potentially inappropriate medications were amitriptyline, dextropropoxyphene, quinine and benzodiazepines. Almost half (49%) of the participants were prescribed at least one potentially inappropriate medication; 29% were considered to suffer significant depressive symptoms (Geriatric Depression Scale ≥5) and no differences were found in the number of inappropriate medications used between those with and without significant depressive symptoms (Chi-square 0.005 p=0.54). DISCUSSION: Potentially inappropriate medication use, as per the modified Beers' Criteria, is very common among community-dwelling older people with depressive symptoms. However, the utility of the Beers' Criteria is lessened by lack of clinical correlation. Ongoing research to examine outcomes related to apparent inappropriate medication use is needed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".