The OptimaMed intervention to reduce medication burden in nursing home residents with severe dementia: results from a pragmatic, controlled study
Bibliographic record
Abstract
BACKGROUND: Nursing home (NH) residents with severe dementia use many medications, sometimes inappropriately within a comfort care approach. Medications should be regularly reviewed and eventually deprescribed. This pragmatic, controlled trial assessed the effect of an interprofessional knowledge exchange (KE) intervention to decrease medication load and the use of medications of questionable benefit among these residents. METHODS: A 6-month intervention was performed in 4 NHs in the Quebec City area, while 3 NHs, with comparable admissions criteria, served as controls. Published lists of "mostly", "sometimes" or "exceptionally" appropriate medications, tailored for NH residents with severe dementia, were used. The intervention included 1) information for participants' families about medication use in severe dementia; 2) a 90-min KE session for NH nurses, pharmacists, and physicians; 3) medication reviews by NH pharmacists using the lists; 4) discussions on recommended changes with nurses and physicians. Participants' levels of agitation and pain were evaluated using validated scales at baseline and the end of follow-up. RESULTS: Seven (7) NHs and 123 participants were included for study. The mean number of regular medications per participant decreased from 7.1 to 6.6 in the intervention, and from 7.7 to 5.9 in the control NHs (p-value for the difference in differences test: < 0.05). Levels of agitation decreased by 8.3% in the intervention, and by 1.4% in the control NHs (p = 0.026); pain levels decreased by 12.6% in the intervention and increased by 7% in the control NHs (p = 0.049). Proportions of participants receiving regular medications deemed only exceptionally appropriate decreased from 19 to 17% (p = 0.43) in the intervention and from 28 to 21% (p = 0.007) in the control NHs (p = 0.22). The mean numbers of regular daily antipsychotics per participant fell from 0.64 to 0.58 in the intervention and from 0.39 to 0.30 in the control NHs (p = 0.27). CONCLUSIONS: This interprofessional intervention to reduce inappropriate medication use in NH residents with severe dementia decreased medication load in both intervention and control NHs, without important concomitant increase in agitation, but mixed effects on pain levels. Practice changes and heterogeneity within these 7 NHs, and a ceiling effect in medication optimization likely interfered with the intervention. TRIAL REGISTRATION: The study is registered at ClinicalTrials.gov: # NCT05155748 (first registration 03-10-2017).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".