CAN DEPRESCRIBING GIVE WHAT POLYPHARMACY HAS TAKEN AWAY? FEASIBILITY TRIAL IN RESIDENTIAL AGED CARE
Bibliographic record
Abstract
Polypharmacy, with its resulting negative health outcomes, is increasing worldwide alongside our ageing population. In specific, anticholinergic and sedative medicines contribute to the decline in cognitive and physical functioning of older people. The Drug Burden Index (DBI) measures the cumulative daily sedative and anticholinergic load. Our aim was to examine the feasibility of reducing the DBI of older people living in residential aged care facilities (RACFs). Residents aged ≥65 years prescribed one or more anticholinergic or sedative medicine, were recruited from three RACFs in New Zealand. A patient-centred approach was implemented; where a clinically trained pharmacist conducted a resident interview and a comprehensive medicine review for each participant. Deprescribing recommendations were put forward to the residents’ general practitioner (GP). We recruited 37 participants with a mean age of 82.8 ± 8.2. Residents were followed up three months after their GP deprescribed one or more of their medicine(s). A Wilcoxon Signed-Rank test indicated that post DBI test ranks, were statistically significantly less than pre DBI test ranks (p= 0.00016) and post-Cognition Performance Score (CPS) test ranks were statistically significantly less than pre-CPS test ranks (p=0.02). In addition, a one-sided paired t-test showed that potential adverse drug reactions (ADRs) decreased by a mean of 2.92 (p=0.0001). Therefore, the pharmacist’s deprescribing intervention resulted in a statistically significant reduction in both residents’ DBI and potential ADRs; as well as an improvement in residents’ cognition. This supports existing research that deprescribing can reverse negative polypharmacy effects and result in several potential health benefits.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".