Interventions to address polypharmacy in older adults living with multimorbidity
Bibliographic record
Abstract
OBJECTIVE: To summarize evidence from published systematic reviews evaluating the effect of polypharmacy interventions on clinical and intermediate outcomes. It also summarizes the adverse events that may occur as a result of these interventions. DATA SOURCES: A literature search was conducted using the electronic databases MEDLINE, Embase, CINAHL, Cochrane Central, and Cochrane Database of Systematic Reviews (PROSPERO registration number: CRD42018085767). STUDY SELECTION: The search yielded a total of 21,329 citations, of which 619 were reviewed as full text and 5 met the selection criteria. SYNTHESIS: The polypharmacy interventions were found to produce statistically significant reductions in potentially inappropriate prescribing and improved medication adherence; however, the observed effects on clinical and intermediate outcomes were inconsistent. None of the included reviews reported any significant benefit of polypharmacy interventions for quality-of-life outcomes. Specific to health care utilization and cost, polypharmacy interventions reduced health care resource usage and expenditure. The reviews reported no differences in adverse drug events between polypharmacy interventions and usual care groups. The overall certainty of evidence was reported as low to very low across included reviews. CONCLUSION: Polypharmacy interventions are associated with reductions in potentially inappropriate prescribing and improvements in medication adherence. However, there is limited evidence of their effectiveness for clinical and intermediate outcomes.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".