SCAI expert consensus statement: Evaluation, management, and special considerations of cardio‐oncology patients in the cardiac catheterization laboratory (Endorsed by the Cardiological Society of India, and Sociedad Latino Americana de Cardiologıa Intervencionista)
Bibliographic record
Abstract
In the United States alone, there are currently approximately 14.5 million cancer survivors, and this number is expected to increase to 20 million by 2020. Cancer therapies can cause significant injury to the vasculature, resulting in angina, acute coronary syndromes (ACS), stroke, critical limb ischemia, arrhythmias, and heart failure, independently from the direct myocardial or pericardial damage from the malignancy itself. Consequently, the need for invasive evaluation and management in the cardiac catheterization laboratory (CCL) for such patients has been increasing. In recognition of the need for a document on special considerations for cancer patients in the CCL, the Society for Cardiovascular Angiography and Interventions (SCAI) commissioned a consensus group to provide recommendations based on the published medical literature and on the expertise of operators with accumulated experience in the cardiac catheterization of cancer patients.
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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 | 0.050 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".