Adverse events in patients with return emergency department visits
Bibliographic record
Abstract
OBJECTIVES: This study describes the proportion of emergency department (ED) returns within 7 days due to adverse events, defined as adverse outcomes related to healthcare received. DESIGN: Prospective cohort study. SETTING: We used an electronically triggered adverse event surveillance system at a tertiary care ED from May to June 2010 to examine ED returns within 7 days of index visit. PARTICIPANTS: One of three trained nurses determined whether the visit was related to index emergency care. For such records, one of three trained emergency physicians conducted adverse event determinations. MAIN OUTCOME MEASURE: We determined adverse event type and severity and analysed the data with descriptive statistics, χ(2) tests and logistic regression. RESULTS: Of 13,495 index ED visits, 923 (6.8%) were followed by ED returns within 7 days. The median age of all patients was 47 years and 52.8% were women. After nursing review, 211 cases required physician review. Of these, 53 visits were adverse events (positive predictive value (PPV)=5.7%, 95% CI 4.4% to 7.4%) and 30 (56.6%) were preventable. Common adverse event types involved management, diagnostic or medication issues. We observed one potentially preventable death and 58.5% of adverse events resulting in transient disability. The PPV of a modified trigger with a cut-off of return within 72 h, resulting in admission was 11.9% (95% CI 6.8% to 18.9%). CONCLUSIONS: Our electronic trigger efficiently identified adverse events among 12% of patients with ED returns within 72 h, requiring hospital admission. Given the high degree of preventability of the identified adverse events, this trigger also holds promise as a performance measurement tool.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".