The <scp>Australian Team Approach to Polypharmacy Evaluation and Reduction</scp> (<scp>AusTAPER</scp>) hospital study: effect of a collaborative medication review on the number of current regular medicines for older hospital inpatients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Potentially harmful polypharmacy is a growing public health concern. This article aims to evaluate the effectiveness of a structured Team Approach to Polypharmacy Evaluation and Reduction (AusTAPER) framework. METHODS: We recruited patients at metropolitan hospitals for a randomised controlled trial with 12 months of follow-up. The intervention included a comprehensive medicines history, multidisciplinary meeting and medicines review prior to discharge, with engagement with the participants' general practitioner extending after discharge. The primary outcome was the change in the number of regular medicines used at 12 months from baseline. A cost consequence was performed to estimate costs per participant during the study period. RESULTS: There were 98 participants enrolled in the study. The number of regular medicines was significantly reduced from baseline in both groups (-1.7 ± 4.3, t = 2.38, P = 0.02 in the control group vs -2.7 ± 3.6, t = 4.48, P = 0.0001 in the intervention group), although there was no statistical difference detected between the two groups (1.0 (SE 0.9), t = 1.03, P = 0.31). The intervention was estimated to cost AU$644.17 and was associated with cost savings of AU$552.53 per participant in sustained reduced medicines cost. Health outcomes and healthcare costs were similar in both groups. DISCUSSION: Medicines were significantly reduced in both groups, with a trend to a larger reduction in medicines at 12 months in the intervention group. The intervention cost was approximately offset by sustained reduced medicines cost, although these results should be regarded cautiously because of the absence of significance in the differences in outcomes between groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".