Impact of Medication Regimen Simplification on Medication Incidents in Residential Aged Care: SIMPLER Randomized Controlled Trial
Bibliographic record
Abstract
In the SImplification of Medications Prescribed to Long-tErm care Residents (SIMPLER) cluster-randomized controlled trial, we investigated the impact of a structured medication regimen simplification intervention on medication incidents in residential aged care facilities (RACFs) over a 12-month follow-up. A clinical pharmacist applied the validated 5-step Medication Regimen Simplification Guide for Residential Aged CarE (MRS GRACE) for 96 of the 99 participating residents in the four intervention RACFs. The 143 participating residents in the comparison RACFs received usual care. Over 12 months, medication incident rates were 95 and 66 per 100 resident-years in the intervention and comparison groups, respectively (adjusted incident rate ratio (IRR) 1.13; 95% confidence interval (CI) 0.53-2.38). The 12-month pre/post incident rate almost halved among participants in the intervention group (adjusted IRR 0.56; 95%CI 0.38-0.80). A significant reduction in 12-month pre/post incident rate was also observed in the comparison group (adjusted IRR 0.67, 95%CI 0.50-0.90). Medication incidents over 12 months were often minor in severity. Declines in 12-month pre/post incident rates were observed in both study arms; however, rates were not significantly different among residents who received and did not receive a one-off structured medication regimen simplification intervention.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".