Impact of the Goal‐directed Medication Review Electronic Decision Support System on Drug Burden Index: A cluster‐randomised clinical trial in primary care
Bibliographic record
Abstract
AIMS: The Goal-directed Medication Review Electronic Decision Support System (G-MEDSS) assesses and reports a patient's goals, attitudes to deprescribing and Drug Burden Index (DBI) score, a measure of cumulative exposure to anticholinergic and sedative medications. This study evaluated the effect of implementing G-MEDSS in home medicines reviews (HMRs) on DBI exposure and clinical outcomes. METHODS: A cluster-randomised clinical trial was performed across Australia. Accredited clinical pharmacists were randomised into intervention (G-MEDSS with usual care HMR) or comparison groups (usual care HMR alone). Patients were recruited by pharmacists from those routinely referred by general practitioners for HMR. The primary outcome was the proportion of patients with any reduction in DBI at 3-months follow-up. Secondary outcomes included change in DBI continuous score at 3-months, HMR recommendations to change DBI and clinical outcomes. RESULTS: There were 201 patient participants at baseline (n = 88 intervention, n = 113 comparison), with 159 followed-up at 3-months (n = 63 intervention, n = 96 comparison). The proportion of patients with a reduction in DBI was not significantly different at 3-months (intervention 17%, comparison 11%; adjusted odds ratio 1.44, 95% confidence interval 0.56-3.80). Regarding secondary outcomes, there was no difference in change in DBI score at 3-months. However, the HMR report made recommendations to reduce DBI for a significantly greater proportion of patients in the intervention than in the comparison group (intervention 37%, comparison 14%; adjusted odds ratio 3.20, 95% confidence interval 1.50-6.90). No changes were observed in clinical outcomes. CONCLUSION: Implementation of G-MEDSS within HMR did not reduce patients' DBI at 3 months compared with usual care HMR.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".