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.
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 | 0.001 | 0.001 |
| 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".