External validation of the <scp>GRACE</scp> risk score 2.0 in the contemporary <scp>all‐comers GLOBAL LEADERS</scp> trial
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess the predictive ability of the Global Registry of Acute Coronary Events (GRACE) risk score 2.0 in contemporary acute coronary syndrome (ACS) patients, and its relation to antiplatelet strategies. BACKGROUND: The predictive value of the GRACE risk score in the contemporary ACS cohort and the appropriate antiplatelet regimen according to the risk remain unclear. METHODS: This is a subgroup analysis of the all-comers, randomized GLOBAL LEADERS trial, comparing ticagrelor monotherapy versus conventional dual-antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI). The GRACE risk score 2.0 with 1-year mortality prediction was implemented. The randomized antiplatelet effect was assessed in predefined three GRACE risk-groups; low-risk (GRACE <109), moderate-risk (GRACE 109-140), and high-risk (GRACE >140). RESULTS: The GRACE risk score was available in 6,594 out of 7,487 ACS patients among whom 1,743, 2,823, and 2,028 patients were classified as low-risk, moderate-risk, and high-risk, respectively. At 1 year, all-cause mortality occurred in 120 patients (1.8%). The discrimination ability of the GRACE model was moderate (C-statistic = 0.742), whereas 1-year mortality risk was overestimated (mean predicted mortality rate: 3.9%; the Hosmer-Lemeshow chi-square: 21.47; p = 0.006). There were no significant interactions between the GRACE risk strata and effects of the ticagrelor monotherapy on ischemic or bleeding outcomes at 1 year compared to the reference strategy. CONCLUSION: The GRACE risk score 2.0 is valuable in discriminating high risk ACS patients, however, the recalibration of the score is recommended for better risk stratification. There is no significant differences in efficacy and safety of ticagrelor monotherapy across the three GRACE risk strata.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".