Team approach to polypharmacy evaluation and reduction: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background Polypharmacy in older adults can be associated with negative outcomes including falls, impaired cognition, reduced quality of life, and general and functional decline. It is not clear to what extent these are reversible if the number of medications is reduced. Primary care does not have a systematic approach for reducing inappropriate polypharmacy, and there are few, if any, approaches that account for the patient’s priorities and preferences. The primary objective of this study is to test the effect of TAPER (Team Approach to Polypharmacy Evaluation and Reduction), a structured operationalized clinical pathway focused on reducing inappropriate polypharmacy. TAPER integrates evidence tools for identifying potentially inappropriate medications, tapering, and monitoring guidance and explicit elicitation of patient priorities and preferences. We aim to determine the effect of TAPER on the number of medications (primary outcome) and health-related outcomes associated with polypharmacy in older adults. Methods We designed a multi-center randomized controlled trial, with the lead implementation site in Hamilton, Ontario. Older adults aged 70 years or older who are on five or more medications will be eligible to participate. A total of 360 participants will be recruited. Participants will be assigned to either the control or intervention arm. The intervention involves a comprehensive multidisciplinary medication review by pharmacists and physicians in partnership with patients. This review will be focused on reducing medication burden, with the assumption that this will reduce the risks and harms of polypharmacy. The control group is a wait list, and control patients will be given appointments for the TAPER intervention at a date after the final outcome assessment. All patients will be followed up and outcomes measured in both groups at baseline and 6 months. Discussion Our trial is unique in its design in that it aims to introduce an operationalized structured clinical pathway aimed to reduce polypharmacy in a primary care setting while at the same time recording patient’s goals and priorities for treatment. Trial registration Clinical Trials.gov NCT02942927. First registered on October 24, 2016.
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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 | 0.057 | 0.063 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.011 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.089 | 0.014 |
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".