Definitions and Clinical Trial Design Principles for Coronary Artery Chronic Total Occlusion Therapies: CTO-ARC Consensus Recommendations
Bibliographic record
Abstract
Over the past 2 decades, chronic total occlusion (CTO) percutaneous coronary intervention has developed into its own subspecialty of interventional cardiology. Dedicated terminology, techniques, devices, courses, and training programs have enabled progressive advancements. However, only a few randomized trials have been performed to evaluate the safety and efficacy of CTO percutaneous coronary intervention. Moreover, several published observational studies have shown conflicting data. Part of the paucity of clinical data stems from the fact that prior studies have been suboptimally designed and performed. The absence of standardized end points and the discrepancy in definitions also prevent consistency and uniform interpretability of reported results in CTO intervention. To standardize the field, we therefore assembled a broad consortium comprising academicians, practicing physicians, researchers, medical society representatives, and regulators (US Food and Drug Administration) to develop methods, end points, biomarkers, parameters, data, materials, processes, procedures, evaluations, tools, and techniques for CTO interventions. This article summarizes the effort and is organized into 3 sections: key elements and procedural definitions, end point definitions, and clinical trial design principles. The Chronic Total Occlusion Academic Research Consortium is a first step toward improved comparability and interpretability of study results, supplying an increasingly growing body of CTO percutaneous coronary intervention evidence.
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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 | 0.473 | 0.515 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.020 | 0.011 |
| Research integrity | 0.030 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".