COPeptin for diagnosis and prediction in Acute Coronary Syndrome (COPACS) Study: design and objectives
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Morawiec B, Kawecki D, Ho L, Tat L, Muller O, Nowalany-Kozielska E. COPeptin for diagnosis and prediction in Acute Coronary Syndrome (COPACS) Study: design and objectives. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2016;12(4):360-363. doi:10.5114/aic.2016.63627. APA Morawiec, B., Kawecki, D., Ho, L., Tat, L., Muller, O., & Nowalany-Kozielska, E. (2016). COPeptin for diagnosis and prediction in Acute Coronary Syndrome (COPACS) Study: design and objectives. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 12(4), 360-363. https://doi.org/10.5114/aic.2016.63627 Chicago Morawiec, Beata, Damian Kawecki, Lam Ho, Lui Chun Tat, Olivier Muller, and Ewa Nowalany-Kozielska. 2016. "COPeptin for diagnosis and prediction in Acute Coronary Syndrome (COPACS) Study: design and objectives". Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej 12 (4): 360-363. doi:10.5114/aic.2016.63627. Harvard Morawiec, B., Kawecki, D., Ho, L., Tat, L., Muller, O., and Nowalany-Kozielska, E. (2016). COPeptin for diagnosis and prediction in Acute Coronary Syndrome (COPACS) Study: design and objectives. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 12(4), pp.360-363. https://doi.org/10.5114/aic.2016.63627 MLA Morawiec, Beata et al. "COPeptin for diagnosis and prediction in Acute Coronary Syndrome (COPACS) Study: design and objectives." Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, vol. 12, no. 4, 2016, pp. 360-363. doi:10.5114/aic.2016.63627. Vancouver Morawiec B, Kawecki D, Ho L, Tat L, Muller O, Nowalany-Kozielska E. COPeptin for diagnosis and prediction in Acute Coronary Syndrome (COPACS) Study: design and objectives. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2016;12(4):360-363. doi:10.5114/aic.2016.63627.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".