Safety and efficiency of emergency department assessment of chest discomfort
Bibliographic record
Abstract
BACKGROUND: Most Canadian emergency departments use an unstructured, individualized approach to patients with chest pain, without data to support the safety and efficiency of this practice. We sought to determine the proportions of patients with chest discomfort in emergency departments who either had acute coronary syndrome (ACS) and were inappropriately discharged from the emergency department or did not have ACS and were held for investigation. METHODS: Consecutive consenting patients aged 25 years or older presenting with chest discomfort to 2 urban tertiary care emergency departments between June 2000 and April 2001 were prospectively enrolled unless they had a terminal illness, an obvious traumatic cause, a radiographically identifiable cause, severe communication problems or no fixed address in British Columbia or they would not be available for follow-up by telephone. At 30 days we assigned predefined explicit outcome diagnoses: definite ACS (acute myocardial infarction [AMI] or definite unstable angina) or no ACS. RESULTS: Of 1819 patients, 241 (13.2%) were assigned a 30-day diagnosis of AMI and 157 (8.6%), definite unstable angina. Of these 398 patients, 21 (5.3%) were discharged from the emergency department without a diagnosis of ACS and without plans for further investigation. The clinical sensitivity for detecting ACS was 94.7% (95% confidence interval [CI] 92.5%- 96.9%) and the specificity 73.8% (95% CI 71.5%- 76.0%). Of the patients without ACS or an adverse event, 71.1% were admitted to hospital or held in the emergency department for more than 3 hours. INTERPRETATION: The current individualized approach to evaluation and disposition of patients with chest discomfort in 2 Canadian tertiary care emergency departments misses 5.3% of cases of ACS while consuming considerable health care resources for patients without coronary disease. Opportunities exist to improve both safety and efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".