Validation of the J-Chronic Total Occlusion Score for Chronic Total Occlusion Percutaneous Coronary Intervention in an Independent Contemporary Cohort
Bibliographic record
Abstract
BACKGROUND: Chronic total occlusion (CTO) recanalization is a complex and technically challenging procedure. The J-CTO score has been proposed to stratify case complexity and procedural success rates. However, the score has never been tested outside the setting of the original study. Moreover, its predictive value when using a hybrid antegrade or retrograde approach is unknown. We investigated the performance of the J-CTO score for predicting procedure complexity and success in an independent contemporary cohort. METHODS AND RESULTS: A total of 209 consecutive patients who underwent CTO recanalization by a high-volume operator were included. Clinical and angiographic data were prospectively collected. The J-CTO score was applied for each patient, and discrimination and calibration were evaluated in the whole cohort, and according to the approach (antegrade 47% and retrograde 53%). Clinical and angiographic differences were noted between the original and studied cohort. The mean J-CTO score was 2.18±1.26, and successful guidewire crossing within 30 minutes and final angiographic success were 44.5% and 90.4%, respectively. The J-CTO score demonstrated good discrimination (c statistic, >0.70) and calibration (Hosmer-Lemeshow P>0.1) in the whole cohort and for antegrade and retrograde approaches. However, the final success rate was not associated with the J-CTO score. CONCLUSIONS: In this independent cohort, the J-CTO score showed good discriminatory and calibration capacity for guidewire CTO crossing within 30 minutes but it does not for final success rate. The J-CTO score helps to predict complexity of CTO recanalization, and the simplicity of the score supports the widespread use as a clinical tool.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".