High-risk medications in older patients with trauma: a cross-sectional study of risk mitigation
Bibliographic record
Abstract
Background: The Beers Criteria for Potentially Inappropriate Medication Use inOlder Adults is a framework that can assess the nature of high-risk medication use. The objective of this study was to use the Beers Criteria to assess the prevalence and nature of polypharmacy, the magnitude of medication changes during the hospital stay and the impact of Beers Criteria medications on outcomes in older patients with trauma. Methods: We used the Alberta Trauma Registry to conduct a retrospective review of patients aged 65 years or older with major trauma (Injury Severity Score ≥ 12) who were admitted to a level 1 trauma centre between January 2013 and December 2014. We analyzed changes in medication prescriptions during the hospital stay using descriptive statistics and assessed the association between Beers Criteria medications and relevant outcomes using multivariable regression analysis. Results: There was no significant change in the number of Beers Criteria medications prescribed before and after admission. The adjusted odds ratio for 30-day mortality for each additional Beers Criteria medication prescribed was 2.02 (95% confidence interval [CI] 1.16–3.51) versus 1.24 (95% CI 1.04–1.59) for each additional medication of any type. The corresponding adjusted incidence rate ratios for length of stay were 1.15 (95% CI 1.03–1.30) versus 1.05 (95% CI 1.01–1.10). Conclusion: Beers Criteria medications were not discontinued during trauma admissions. Using the Beers Criteria as a process indicator for quality improvement in trauma care may provide interdisciplinary trauma teams an opportunity to audit patient medications and stop potentially harmful medications in a vulnerable population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".