Deprescribing to optimise health outcomes for frail older people: a double-blind placebo-controlled randomised controlled trial—outcomes of the Opti-med study
Bibliographic record
Abstract
BACKGROUND: potentially harmful polypharmacy is very common in older people living in aged care facilities. To date, there have been no double-blind randomised controlled studies of deprescribing multiple medications. METHODS: three-arm (open intervention, blinded intervention and blinded control) randomised controlled trial enrolling people aged over 65 years (n = 303, noting pre-specified recruitment target of n = 954) living in residential aged care facilities. The blinded groups had medications targeted for deprescribing encapsulated while the medicines were deprescribed (blind intervention) or continued (blind control). A third open intervention arm had unblinded deprescribing of targeted medications. RESULTS: participants were 76% female with mean age 85.0 ± 7.5 years. Deprescribing was associated with a significant reduction in the total number of medicines used per participant over 12 months in both intervention groups (blind intervention group -2.7 medicines, 95% CI -3.5, -1.9, and open intervention group -2.3 medicines; 95% CI -3.1, -1.4) compared with the control group (-0.3, 95% CI -1.0, 0.4, P = 0.053). Deprescribing regular medicines was not associated with any significant increase in the number of 'when required' medicines administered. There were no significant differences in mortality in the blind intervention group (HR 0.93, 95% CI 0.50, 1.73, P = 0.83) or the open intervention group (HR 1.47, 95% CI 0.83, 2.61, P = 0.19) compared to the control group. CONCLUSIONS: deprescribing of two to three medicines per person was achieved with protocol-based deprescribing during this study. Pre-specified recruitment targets were not met, so the impact of deprescribing on survival and other clinical outcomes remains uncertain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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".