Iatrogenic Adverse Events in the Coronary Care Unit
Bibliographic record
Abstract
BACKGROUND: Although our understanding of medical adverse events has increased substantially over the last decade, little is known about iatrogenic adverse events (IAEs) in the coronary care unit (CCU). We sought to determine the frequency and potential preventability of IAEs in the CCU of a tertiary care center. METHODS AND RESULTS: We undertook a retrospective cohort study evaluating the hospital charts of consecutive patients admitted to the CCU at Hamilton General Hospital (Ontario, Canada) from November 1, 2005, to January 1, 2006. We used a priori developed definitions to determine whether patients suffered an IAE and whether it was potentially preventable. We included 194 patients, and 99 (51%; 95% CI, 44% to 58%) patients had at least 1 IAE, of which 45 (45%; 95% CI, 36% to 55%) were judged potentially preventable. Bleeding (14/51, 27%; 95% CI, 17% to 41%) was the most common potentially preventable IAE and was more common than recurrent ischemic events (4/51, 8%; 95% CI, 3% to 19%). Of the patients who died in the hospital, 9 of 17 (53%; 95% CI, 31% to 74%) had an IAE that was causally related to their death, of which 6 (67%; 95% CI, 35% to 88%) were judged potentially preventable. CONCLUSIONS: The present study suggests that IAEs, especially bleeding, are common in the CCU setting and more frequent than recurrent ischemic events. These results suggest the need for large multicenter studies to evaluate in CCUs the rates of IAEs, their causes, and potential preventability.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".