External Validation of the PRECISE‐DAPT Cancer Score in Patients With Acute Myocardial Infarction
Bibliographic record
Abstract
AIMS: We aimed to externally validate the PRECISE-DAPT cancer score which showed better accuracy in predicting bleeding events in patients with cancer than the original PRECISE-DAPT score. METHODS: We used data from the BleeMACS (Bleeding complications in a Multicenter registry of patients discharged after an Acute Coronary Syndrome) project. We compared the performance and clinical usefulness of the original score and the cancer score by calculating the C-statistic, the net reclassification index (NRI), and decision curve analysis. RESULTS: A total of 13,932 patients were included, of which 864 patients had a diagnosis of cancer at the time of presentation with an AMI. According to the original PRECISE DAPT score, 63.3% of patients with cancer were classified as HBR, whereas 94.9% of patients with cancer were classified as HBR according to the cancer score. Cox-regression models showed that patients classified as HBR by the updated cancer score have higher odds of bleeding (HR 2.6, 95% CI 2.1-3.1) events than patients classified as HBR by the original score (HR 2.2, 95% CI 1.8-2.7). The cancer score showed higher discrimination ability (C-statistic 0.66) than the original score (C-statistic 0.64). The overall NRI of the cancer score was 2.7%. The decision curves analysis showed that the cancer score use is roughly identical to the original score in patients without cancer but superior to the original score in patients with cancer. CONCLUSION: The PRECISE-DAPT cancer score is a valid and useful tool for the prediction of bleeding risk in patients with cancer and presenting with AMI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".