Endovascular treatment of a traumatic ventricular septal rupture and coronary to ventricular fistula
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Topuz M, Çaylı M, Cosgun M. Endovascular treatment of a traumatic ventricular septal rupture and coronary to ventricular fistula. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2016;12(3):276-277. doi:10.5114/aic.2016.61654. APA Topuz, M., Çaylı, M., & Cosgun, M. (2016). Endovascular treatment of a traumatic ventricular septal rupture and coronary to ventricular fistula. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 12(3), 276-277. https://doi.org/10.5114/aic.2016.61654 Chicago Topuz, Mustafa, Murat Çaylı, and Mehmet Cosgun. 2016. "Endovascular treatment of a traumatic ventricular septal rupture and coronary to ventricular fistula". Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej 12 (3): 276-277. doi:10.5114/aic.2016.61654. Harvard Topuz, M., Çaylı, M., and Cosgun, M. (2016). Endovascular treatment of a traumatic ventricular septal rupture and coronary to ventricular fistula. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 12(3), pp.276-277. https://doi.org/10.5114/aic.2016.61654 MLA Topuz, Mustafa et al. "Endovascular treatment of a traumatic ventricular septal rupture and coronary to ventricular fistula." Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, vol. 12, no. 3, 2016, pp. 276-277. doi:10.5114/aic.2016.61654. Vancouver Topuz M, Çaylı M, Cosgun M. Endovascular treatment of a traumatic ventricular septal rupture and coronary to ventricular fistula. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2016;12(3):276-277. doi:10.5114/aic.2016.61654.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".