Potentially inappropriate medication and attitudes of older adults towards deprescribing
Bibliographic record
Abstract
INTRODUCTION: Multimorbidity and polypharmacy are current challenges when caring for the older population. Both have led to an increase of potentially inappropriate medication (PIM), illustrating the need to assess patients' attitudes towards deprescribing. We aimed to assess the prevalence of PIM use and whether this was associated with patient factors and willingness to deprescribe. METHOD: We analysed data from the LESS Study, a cross-sectional study on self-reported medication and on barriers and enablers towards the willingness to deprescribe (rPATD questionnaire). The survey was conducted among multimorbid (≥3 chronic conditions) participants ≥70 years with polypharmacy (≥5 long-term medications). A subset of the Beers 2019 criteria was applied for the assessment of medication appropriateness. RESULTS: Data from 300 patients were analysed. The mean age was 79.1 years (SD 5.7). 53% had at least one PIM (men: 47.8%%, women: 60.4%%; p = 0.007). A higher number of medications was associated with PIM use (p = 0.002). We found high willingness to deprescribe in both participants with and without PIM. Willingness to deprescribe was not associated with PIM use (p = 0.25), nor number of PIMs (p = 0.81). CONCLUSION: The willingness of older adults with polypharmacy towards deprescribing was not associated with PIM use in this study. These results suggest that patients may not be aware if they are taking PIMs. This implies the need for raising patients' awareness about PIMs through education, especially in females, in order to implement deprescribing in daily practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".