Definitions and Clinical Trial Design Principles for Coronary Artery Chronic Total Occlusion Therapies: CTO-ARC Consensus Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 it