Knowledge Translation Strategy to Reduce the Use of Potentially Inappropriate Medications in Hospitalized Elderly Adults
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effect of a knowledge translation (KT) strategy to reduce potentially inappropriate medication (PIM) use in hospitalized elderly adults. DESIGN: Segmented regression analysis of an interrupted time series. SETTING: Teaching hospital. PARTICIPANTS: Individuals aged 75 and older discharged from the hospital in 2013/14 (mean age 83.3, 54.5% female). INTERVENTION: The KT strategy comprises the distribution of educational materials, presentations by geriatricians, pharmacist-physician interventions based on alerts from a computerized alert system, and comprehensive geriatric assessments. MEASUREMENTS: Rate of PIM use (number of patient-days with use of at least one PIM/number of patient-days of hospitalization for individuals aged ≥75). RESULTS: For 8,622 patients with 14,071 admissions, a total of 145,061 patient-days were analyzed. One or more PIMs were prescribed on 28,776 (19.8%) patient-days; a higher rate was found for individuals aged 75 to 84 (24.0%) than for those aged 85 and older (14.4%) (P < .001), and in women (20.8%) than in men (18.6%) (P < .001). The drug classes most frequently accounting for the PIM were gastrointestinal agents (21%), antihistamines (18%), and antidepressants (17%). An absolute decrease of 3.5% (P < .001) of patient-days with at least one PIM was observed immediately after the intervention. CONCLUSION: A KT strategy resulted in decreased use of PIM in elderly adults in the hospital. Additional interventions will be implemented to maintain or further reduce PIM use.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".