Diagnostic accuracy of the TIMI risk score in patients with chest pain in the emergency department: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The Thrombolysis in Myocardial Infarction (TIMI) risk score uses clinical data to predict the short-term risk of acute myocardial infarction, coronary revascularization or death from any cause. It was originally developed for use in patients with unstable angina or non-ST-elevation myocardial infarction. We sought to expand the clinical application of the TIMI risk score by assessing its prognostic accuracy in patients in the emergency department with potential acute coronary syndromes. METHODS: We searched five electronic databases, hand-searched reference lists of included studies and contacted content experts to identify articles for review. We included prospective cohort studies that validated the TIMI risk score in emergency department patients. We performed a meta-regression to determine whether a linear relation exists between TIMI risk score and the cumulative incidence of cardiac events. RESULTS: We included 10 prospective cohort studies (with a total of 17 265 patients) in our systematic review. Data were available for meta-analysis in 8 of the 10 studies. Of patients with a score of zero, 1.8% had a cardiac event within 30 days (sensitivity 97.2%, 95% CI 96.4-97.8; specificity 25.0%, 95% CI 24.3-25.7; positive likelihood ratio 1.30, 95% CI 1.28-1.31; negative likelihood ratio 0.11, 95% CI 0.09-0.15). Meta-regression analysis revealed a strong linear relation between TIMI risk score (p < 0.001) and the cumulative incidence of cardiac events. INTERPRETATION: Although the TIMI risk score is an effective risk stratification tool for patients in the emergency department with potential acute coronary syndromes, it should not be used as the sole means of determining patient disposition.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.067 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".