Ottawa Hospital Patient Safety Study: incidence and timing of adverse events in patients admitted to a Canadian teaching hospital
Bibliographic record
Abstract
BACKGROUND: Adverse events are poor patient outcomes that are due to medical care. Studies of hospital patients have demonstrated that adverse events are common, but few data describe the timing of them in relation to hospital admission. We evaluated characteristics of adverse events affecting patients admitted to a Canadian teaching hospital, paying particular attention to timing. METHODS: We randomly selected 502 adults admitted to the Ottawa Hospital for acute care of nonpsychiatric illnesses over a 1-year period. Charts were reviewed in 2 stages. If an adverse event was judged to have occurred, a physician determined whether it occurred before or during the index hospitalization. The reviewer also rated the preventability, severity and type of each adverse event. RESULTS: Of the 64 patients with an adverse event (incidence 12.7%, 95% confidence interval [CI] 10.1%-16.0%), 24 had a preventable event (4.8%, 95% CI 3.2%-7.0%), and 3 (0.6%, 95% CI 0.2%-1.7%) died because of an adverse event. Most adverse events were due to drug treatment, operative complications or nosocomial infections. Of the 64 patients, 39 (61%, 95% CI 49%-72%) experienced the adverse event before the index hospitalization. INTERPRETATION: Adverse events were common in this study. However, only one-third were deemed avoidable, and most occurred before the hospitalization. Interventions to improve safety must address ambulatory care as well as hospital-based care.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".