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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".