Interventions to improve medicines optimisation in older people with frailty in primary care: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To identify studies that delivered an intervention to frail older people to improve medicines optimisation; identify the outcomes reported in these studies; and assess the effectiveness of these interventions on chosen study outcomes. METHOD: Eight electronic databases and four trial registries were systematically searched from the date of inception to April 2020. Inclusion criteria were randomised controlled trials and non-randomised studies of interventions to improve medicines optimisation (including administration, adherence, deprescribing, prescribing and/or medication review) in community-dwelling older people (aged ≥65 years) with a frailty diagnosis. Only studies published in English were included. A narrative synthesis was conducted, and quality was assessed using an appropriate risk of bias tool. KEY FINDINGS: Searches identified 601 articles; one study met the criteria for inclusion. The single eligible study used a quasi-experimental pre-test-post-test study design to evaluate the impact of a pharmacist-led, team-based medication review for 54 frail older patients living in primary care. Improvements in the total number of medications and prescribing appropriateness were observed. The study was judged to be at an overall serious risk of bias. CONCLUSION: There is a dearth of high-quality evidence demonstrating the effectiveness of medicines optimisation interventions for older people with frailty within primary care. Due to the strong association between patients' level of frailty and adverse outcomes, it is important that future research focuses on proactive interventions which may be beneficial to this patient population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".