CIHR Research: Addressing the Effects of Adverse Events: Study Provides Insights into Patient Safety at Canadian Hospitals
Bibliographic record
Abstract
We live in an exciting era, where new therapeutic discoveries move quickly from the research bench to the patient bedside.Yet in implementing these discoveries and providing care, defences sometimes fail, resulting in a preventable adverse event.On May 25, 2004, the first national study to examine the problem of adverse events in Canadian hospitals, led by the authors of this paper and involving researchers from seven Canadian universities, was published in the Canadian Medical Association Journal (CMAJ).Funded by the Canadian Institutes of Health Research (CIHR) and the Canadian Institute for Health Information (CIHI), the Canadian Adverse Events Study found that, in 2000, the overall rate of adverse events was 7.5 per 100 patients admitted, not including pediatric, obstetric and psychiatric admissions.In other words, approximately 185,000 of the 2.5 million similar medical and surgical admissions in Canadian hospitals in 2000 were associated with an adverse event.In the study, we used a definition of adverse event that has been applied to similar studies elsewhere.An adverse event is an "unintended injury or complication resulting in death, disability or prolonged hospital stay caused by healthcare management rather than the patient's underlying condition."
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".