Results of an international crowdsourcing survey on the treatment of non-ST segment elevation ACS patients at high-bleeding risk undergoing percutaneous intervention
Bibliographic record
Abstract
Aims Choosing an antiplatelet strategy in patients with non-ST segment elevation acute coronary syndrome (NSTE-ACS) at high bleeding risk (HBR), undergoing post-percutaneous coronary intervention (PCI), is complex. We used a unique open-source approach (crowdsourcing) to document if practices varied across a small, global cross-section of antiplatelet prescribers in the post-PCI setting. Methods and results Five-hundred and fifty-nine professionals from 70 countries (the ‘crowd') completed questionnaires containing single- or multi-option and free form questions regarding antiplatelet clinical practice in post-PCI NSTE-ACS patients at HBR. A threshold of 75% defined ‘agreement'. There was strong agreement favouring monotherapy with either aspirin or a P2Y 12 inhibitor following initial DAPT, within the first year (94%). No agreement was reached on the optimal duration of DAPT or choice of monotherapy: responses were in equipoise for shorter (≤3 months, 51%) or longer (≥6 months, 46%) duration, and monotherapy choice (45% aspirin; 53% P2Y 12 inhibitor). Most respondents stated use of guideline-directed tools to assess risk, although clinical judgement was preferred by 32% for assessing bleeding risk and by 46% for thrombotic risk. Conclusion The crowdsourcing methodology showed potential as a tool to assess current practice and variation on a global scale and to achieve a broad demographic representation. These preliminary results indicate a high degree of variation with respect to duration of DAPT, monotherapy drug of choice following DAPT and how thrombotic and bleeding risk are assessed. Further investigations should concentrate on interrogating practice variation between key demographic groups.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".