Interventions to improve the appropriate use of polypharmacy in older people: a Cochrane systematic review
Bibliographic record
Abstract
OBJECTIVE: To summarise the findings of an updated Cochrane review of interventions aimed at improving the appropriate use of polypharmacy in older people. DESIGN: Cochrane systematic review. Multiple electronic databases were searched including MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (from inception to November 2013). Hand searching of references was also performed. Randomised controlled trials (RCTs), controlled clinical trials, controlled before-and-after studies and interrupted time series analyses reporting on interventions targeting appropriate polypharmacy in older people in any healthcare setting were included if they used a validated measure of prescribing appropriateness. Evidence quality was assessed using the Cochrane risk of bias tool and GRADE (Grades of Recommendation, Assessment, Development and Evaluation). SETTING: All healthcare settings. PARTICIPANTS: Older people (≥ 65 years) with ≥ 1 long-term condition who were receiving polypharmacy (≥ 4 regular medicines). PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were the change in prevalence of appropriate polypharmacy and hospital admissions. Medication-related problems (eg, adverse drug reactions), medication adherence and quality of life were included as secondary outcomes. RESULTS: 12 studies were included: 8 RCTs, 2 cluster RCTs and 2 controlled before-and-after studies. 1 study involved computerised decision support and 11 comprised pharmaceutical care approaches across various settings. Appropriateness was measured using validated tools, including the Medication Appropriateness Index, Beers' criteria and Screening Tool of Older Person's Prescriptions (STOPP)/ Screening Tool to Alert doctors to Right Treatment (START). The interventions demonstrated a reduction in inappropriate prescribing. Evidence of effect on hospital admissions and medication-related problems was conflicting. No differences in health-related quality of life were reported. CONCLUSIONS: The included interventions demonstrated improvements in appropriate polypharmacy based on reductions in inappropriate prescribing. However, it remains unclear if interventions resulted in clinically significant improvements (eg, in terms of hospital admissions). Future intervention studies would benefit from available guidance on intervention development, evaluation and reporting to facilitate replication in clinical 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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".