PRECISE-DAPT score for bleeding risk prediction in patients on dual or single antiplatelet regimens: insights from the GLOBAL LEADERS and GLASSY
Bibliographic record
Abstract
AIMS: The five-item PRECISE-DAPT, integrating age, haemoglobin, white-blood-cell count, creatinine clearance, and prior bleeding, predicts bleeding risk in patients on dual antiplatelet therapy (DAPT) after stent implantation. We sought to assess whether the bleeding risk prediction offered by the PRECISE-DAPT remains valid among patients receiving ticagrelor monotherapy from 1 month onwards after coronary stenting instead of standard DAPT and having or not having centrally adjudicated bleeding endpoints. METHODS AND RESULTS: The PRECISE-DAPT was calculated in 14 928 and 7134 patients from GLOBAL LEADERS and GLASSY trials, respectively. The ability of the score to predict Bleeding Academic Research Consortium 3 or 5 bleeding was assessed and compared among patients on ticagrelor monotherapy (experimental strategy) or standard DAPT (reference strategy) from 1 month after drug-eluting stent implantation. Bleeding endpoints were investigator-reported or centrally adjudicated in GLOBAL LEADERS and GLASSY, respectively. At 2 years, the c-indexes for the score among patients treated with the experimental or reference strategy were 0.67 [95% confidence interval (CI): 0.63-0.71] vs. 0.63 (95% CI: 0.59-0.67) in GLOBAL LEADERS (P = 0.27), and 0.67 (95% CI: 0.61-0.73) vs. 0.66 (95% CI: 0.61-0.72) in GLASSY (P = 0.88). Decision curve analysis showed net benefit using the PRECISE-DAPT to guide bleeding risk assessment under both treatment strategies. Results were consistent between investigator-reported and adjudicated endpoints and using the simplified four-item PRECISE-DAPT. CONCLUSION: The PRECISE-DAPT offers a prediction model that proved similarly effective to predict clinically relevant bleeding among patients on ticagrelor monotherapy from 1 month after coronary stenting compared with standard DAPT and appears to be unaffected by the presence or absence of adjudicated bleeding endpoints.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".