The Difference in Accuracy Between Global Registry of Acute Coronary Events Score and Thrombolysis in Myocardial Infarction Score in Predicting In-Hospital Mortality of Acute ST-Elevation Myocardial Infarction Patients
Bibliographic record
Abstract
BACKGROUND: In-hospital mortality of ST-elevation myocardial infarction (STEMI) patients varies between 1% and 19% in Asia. Global Registry of Acute Coronary Events (GRACE) score and Thrombolysis in Myocardial Infarction (TIMI) score are the most frequently used risk scores for predicting in-hospital mortality. These two scores have different accuracy depending on the risk profiles of each region. This study aimed to identify the difference in accuracy between GRACE and TIMI scores. METHODS: This was an observational cohort retrospective study on consecutive patients with STEMI admitted to Dr. Hasan Sadikin General Hospital Bandung between July 2018 and June 2019. RESULTS: The risk scores were evaluated in 255 patients with STEMI, whose data were collected from medical records. Patients in this study were 58 ± 11 years old, more often male (78.8%) and have smoking (65.5%), dyslipidemia (61%), hypertension (56.5%) and diabetes mellitus (21.6 %) as their risk factors. Forty-five patients died in hospitalization (17%). The TIMI and GRACE scores revealed a significant graded increase in mortality with a rising score. There was a statistically significant difference in accuracy between the scores of 0.082 (95% confidence interval (CI): 0.040 - 0.125; P < 0.001) with the GRACE score (C statistics of 0.91; P < 0.001) having better accuracy compared to TIMI score (C statistics of 0.83; P < 0.001). This might be due to the fact that the GRACE scoring system has more detail and complete variables than the TIMI score. CONCLUSION: There is a significant difference between the accuracy of GRACE and TIMI scores in predicting in-hospital mortality in STEMI patients. The accuracy of the GRACE score is better than the TIMI score for predicting in-hospital mortality in STEMI patients.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".