Deprescribing Potentially Inappropriate Medications in a Tertiary Care Centre in Ontario
Bibliographic record
Abstract
We evaluated deprescribing practices of potentially inappropriate medications (PIMs) on an Internal Medicine ward in Kingston, Ontario. Methods A retrospective chart review was conducted on patients who were 65 years or older, on 5 or more medications, and hospitalized between November 1, 2017 – December 15, 2017. Medications listed in the 2015 Beer’s Criteria and opioids without a cancer diagnosis were marked as PIMs. Discharge records were used to identify PIMs that were deprescribed. Results This study included 157 patients (56.1% female). In total, 234 of 1482 (15.8%) of all medications across all patients were identified as PIMs, and 15.4% of these were deprescribed. The top deprescribed medications were antihypertensives and opioids. Nearly half of the documented deprescribing occurred because of an adverse event. Conclusion Less than 20% of PIMs are being discontinued or down-titrated in hospital. This appears to be largely reactionary and driven primarily by adverse events.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".