Acceptability of Interventions to Address Polypharmacy in Older Adult Outpatients: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background and Aims Interventions to address potentially inappropriate prescribing (PIP), where risks outweigh benefits, are effective but often not implemented due to barriers (e.g., patient, provider, systems). Concerns about questioning healthcare providers or symptom resurgence when discontinuing medications may make PIP interventions less acceptable. This systematic review aims to determine the acceptability of PIP interventions among older adult outpatients. Methods We searched MEDLINE, Embase, and other databases for controlled studies of PIP interventions including older adults (≥ 65 years) residing in community or care home settings. The review included interventions aimed at reducing PIP, whether clinical or external providers. We assessed risk of bias and performed a meta‐analysis. Results Nine studies (n = 4,843) were included: six randomized controlled trials, two prospective cohort studies, and one pre‐post study. Studies spanned the US, England, Ireland, Lebanon, the Netherlands, Spain, and Switzerland. Seven out of nine (78%) studies were assessed as having a low risk of bias; two out of nine (22%) at moderate risk. Meta‐analysis showed no significant difference in patient satisfaction between PIP interventions and standard care, though satisfaction was slightly higher with PIP interventions (SMD 0.45; 95% CI −0.14 to 1.04, I² = 96%, n = 4,414). Meta‐analysis showed more patients discussed discontinuing medications with their prescriber after a PIP intervention (RR 4.32; 95% CI 0.0 to 56,270, I² = 43%, n = 429). Conclusion PIP interventions are as acceptable to patients as usual care, despite some burden for patients and prescribers. Patients are more willing to engage in deprescribing conversations when a deprescribing intervention is present.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".