Drug‐Related Hospitalizations in a Tertiary Care Internal Medicine Service of a Canadian Hospital: A Prospective Study
Bibliographic record
Abstract
STUDY OBJECTIVES: To determine the frequency, severity, preventability, and classification of adverse drug events resulting in hospitalization, and to identify any patient, prescriber, drug, and system factors associated with these events. DESIGN: Prospective, observational study. SETTING: Internal medicine service of a large tertiary care hospital in Canada. PATIENTS: A total of 565 consecutive adult patients admitted to the hospital during a 12-week period. MEASUREMENTS AND MAIN RESULTS: A patient's hospitalization was defined as drug related if it was directly related to one of eight predefined classifications; severity and preventability of the hospitalization were also assessed. Multivariate logistic regression analysis was used to evaluate patient, prescriber, drug, and system factors associated with drug-related hospitalizations. The frequency of drug-related hospitalization was 24.1% (95% confidence interval [CI] 20.6-27.8%), of which 72.1% (95% CI 63.7-79.4%) were deemed preventable. Severity was classified as mild, moderate, severe, and fatal in 8.1% (95% CI 4.1-14.0%), 83.8% (95% CI 76.5-89.6%), 7.4% (95% CI 3.6-13.1%), and 0.7% (95% CI 0.0-4.0%), respectively, of the hospitalizations. The most common classifications of drug-related hospitalization were adverse drug reactions (35.3% [95% CI 27.3-43.9%]), improper drug selection (17.6% [95% CI 11.6-25.1%]), and noncompliance (16.2% [95% CI 10.4-23.5%]). No independent risk factors for drug-related hospitalization were identified with regression modeling. CONCLUSION: Approximately 25% of patients in our study were hospitalized for drug-related causes; over 70% of these causes were deemed preventable. Drug-related hospitalization is a significant problem that merits further research and intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".