Impact of crossing strategy on midterm outcomes following percutaneous revascularisation of coronary chronic total occlusions
Bibliographic record
Abstract
AIMS: The aim of the present study was to compare the midterm clinical outcomes of patients undergoing successful chronic total occlusion (CTO) percutaneous coronary intervention (PCI) according to the crossing technique used, in a large multicentre registry. METHODS AND RESULTS: We compiled a multicentre registry of consecutive patients undergoing successful CTO PCI. Patients were divided into three groups: true-to-true (TTT) approach, modern dissection/re-entry (DR) techniques (CrossBoss/Stingray, reverse CART), and old DR techniques (LAST, STAR, CART). Cox regression was used to identify independent predictors of major adverse cardiac events (MACE: cardiac death, myocardial infarction and target vessel revascularisation). We included 924 patients (TTT, n=571; modern DR, n=258; old DR, n=95). Patients in both DR groups had a higher prevalence of comorbidities, angiographic and procedural complexity. The 12-month MACE rate was higher in old DR (22.1%) than in modern DR (8.9%) and TTT (9.1%, p<0.001). Old (hazard ratio [HR] 2.02, 95% confidence interval [CI]: 1.12 to 3.61, p=0.02) but not modern (HR 0.98, 95% CI: 0.54 to 1.79, p=0.96) DR techniques were associated with a higher adjusted risk of MACE compared to TTT. CONCLUSIONS: The use of old but not modern DR techniques was associated with a higher risk of MACE. Therefore, CrossBoss/Stingray and reverse CART might be considered as first-line strategies for antegrade and retrograde DR-based CTO PCI, respectively.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".