Interventions to improve medicines optimisation in frail older patients in secondary and acute care settings: a systematic review of randomised controlled trials and non-randomised studies
Bibliographic record
Abstract
BACKGROUND: Frailty is a geriatric syndrome in which physiological systems have decreased reserve and resistance against stressors. Frailty is associated with polypharmacy, inappropriate prescribing and unfavourable clinical outcomes. AIM: To identify and evaluate randomised controlled trials (RCTs) and non-randomised studies of interventions designed to optimise the medications of frail older patients, aged 65 years and over, in secondary or acute care settings. METHOD: Literature searches were conducted across seven electronic databases and three trial registries from the date of inception to October 2021. All types of interventional studies were included. Study selection, data extraction, risk of bias and quality assessment were conducted by two independent reviewers. RESULTS: Three RCTs were eligible for inclusion; two employed deprescribing as the intervention, and one used comprehensive geriatric assessment. All reported significant improvements in prescribing appropriateness. One study investigated the effect of the intervention on clinical outcomes including hospital presentations, falls, fracture, quality of life and mortality, and reported no significant differences in these outcomes, but did report a significant reduction in monthly medication cost. Two of the included studies were assessed as having 'some concerns' of bias, and one was judged to be at 'high risk' of bias. CONCLUSION: This systematic review demonstrates that medicines optimisation interventions may improve medication appropriateness in frail older inpatients. However, it highlights the paucity of high-quality evidence that examines the impact of medicines optimisation on quality of prescribing and clinical outcomes for frail older inpatients. High-quality studies are needed to address this gap.
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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 | 0.039 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".