033 Multi-component support interventions for deprescribing potentially inappropriate medications in older adults: a systematic review
Bibliographic record
Abstract
Background Inappropriate use of medication is a problem worldwide, especially among older adults. It is associated with adverse drug reactions, hospitalisations, increased healthcare costs, and increased mortality. Despite the harms associated with the inappropriate use of medications, there were an estimated 7.3 billion doses of potentially inappropriate medications consumed by older people in the United States alone in 2018, with similar estimates observed globally. Computerised decision support and patient education have been found to reduce potentially inappropriate prescriptions in older adults and improve communication about the use of medication. However, there has been no systematic review of the effectiveness of multi-component interventions on deprescribing. Objectives To evaluate the effectiveness of multi-component interventions for deprescribing potentially inappropriate medications in older adults and to determine the effects of these interventions on patient-reported outcomes, clinical outcomes and system outcomes. Methods We will search for MEDLINE, Embase, CINAHL, Scopus and Web of Science for studies comparing multi-component interventions to no intervention, sham intervention, or usual care. We will include all trials of older adults >65 years. The interventions will include multi-component interventions to deprescribe potentially inappropriate medications with at least one intervention component directed at clinicians (eg, electronic decision support) and one directed at patients (eg, patient education materials such as brochures). The primary outcome will be the proportion of patients or encounters where one or more potentially inappropriate medications are deprescribed. Medications are considered potentially inappropriate if the potential risks outweigh the clinical benefits in patients, as determined by the study authors. Secondary outcomes will include patient-reported outcomes such as changes in pain or symptoms; clinical outcomes such as quality of life, adverse drug events and adverse drug withdrawal events; and system outcomes such as hospitalisations or emergency department visits. Results The study is underway. We will have the full results ready for the conference. Conclusions This systematic review will provide evidence on multi-component interventions for deprescribing potentially inappropriate medications. The results of this review may have important implications for researchers and policymakers in planning and designing deprescribing interventions as well as improving patient outcomes. The results may also help clinicians better communicate about the benefits and harms of medications with patients.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".