Pharmacist-led intervention to improve medication use in older inpatients using the Drug Burden Index: a study protocol for a before/after intervention with a retrospective control group and multiple case analysis
Bibliographic record
Abstract
Introduction Polypharmacy and potentially inappropriate medication use is common in older adults and is associated with adverse outcomes such as falls and hospitalisations. Methods and analysis This study is a pharmacist-led medication optimisation initiative using an electronic tool (the Drug Burden Index (DBI) Calculator) in four hospital sites in the Canadian province of Nova Scotia. The study aims to enrol 160 participants between the preintervention and intervention groups. The Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT 2013 checklist) was used to develop the protocol for this prospective interventional implementation study. A preintervention retrospective control cohort and a multiple case study analysis will also be used to assess the effect of intervention implementation. Statistical analysis will involve change in DBI scores and assessment of clinical outcomes, such as rehospitalisation and mortality using appropriate statistical tests including t-test, χ2, analysis of variance and unadjusted and adjusted regression methods. Ethics and dissemination Ethics approval has been granted by the Nova Scotia Health Authority Research Ethics Board. The findings of this study will be published in peer-reviewed journals and presented at local, national and international conferences. Trial registration number NCT03698487 .
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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 | 0.047 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".