NAVIGATING POLYPHARMACY IN AGING POPULATIONS: A SYSTEMATIC REVIEW OF DEPRESCRIBING INTERVENTIONS AND ITS IMPACT ON CLINICAL OUTCOMES
Bibliographic record
Abstract
Background: Polypharmacy, prescription of five or more drugs, is common in geriatric care and has been linked to increased risk of adverse drug reactions (ADRs), hospitalization, and reduced quality of life. Deprescribing, a structured approach to supervised discontinuation of inappropriate medications, is a growing trend but remains a debatable notion due to lack of consensus and literature gaps. The objective of this narrative review was to summarize the evidence related to the impact of deprescribing interventions on clinical outcomes. Methods: Systematic searching of PubMed, Cochrane Library, ClinicalTrials.gov, and the WHO International Clinical Trials Registry was done. Studies were then categorized according to study designs with 10 randomized controlled trials (RCTs), 7 cohort studies, and 11 systematic reviews. Studies without a comparator group, qualitative studies, and case reports were excluded. Due to the given heterogeneity of studies, a narrative synthesis of study results was done, and outcomes were summarized according to subgroups (patient characteristics, intervention type and setting). Four major outcomes were assessed, which included adverse drug reactions, hospitalization rates, medication Burden, and Quality of Life. Bias was assessed according to the Newcastle-Ottawa Scale for observational studies and Cochrane Risk of Bias Tool (ROB 2) for RCTs. Results: Results of the Deprescribing interventions in older adults have yielded mixed outcomes across various health parameters. While many studies highlight various benefits of deprescribing such as reductions in adverse drug reactions (ADRs) and medication burden (McDonald et al., 2022; Quek et al., 2024), other studies report limited or no significant effects on hospitalization rates and quality of life (Ibrahim et al., 2021; Jackson & Patel, 2020). Differences in various study designs, populations, and methodologies of the included studies may lead to these inconsistencies. Therefore, standardized protocols and further research is imperative to fully recognize and optimize the effect of deprescribing interventions. Conclusion: Despite the advantages of deprescribing, heterogeneity of protocols, inconsistent reporting of outcomes, and short follow-ups limit the evidence. Standardized guidelines and longer studies are required to optimize deprescribing. Keywords: deprescribing interventions; clinical outcomes; narrative synthesis, older adults; polypharmacy; systematic review
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".