Extended follow-up of the Essential Pro paclitaxel drug-eluting balloon for in-stent restenosis
Bibliographic record
Abstract
To the Editor, The use of drug-eluting balloons (DEB) represents a novel and growing alternative therapeutic strategy for patients with in-stent restenosis (ISR).1 A recent publication in REC: Interventional Cardiology presented the real-world safety and efficacy data on the use of the Essential Pro (iVascular, Spain) DEB in patients with ISR.2 A potential limitation of such analysis is that overall outcome event rates may be influenced by those treated more recently with short-term follow-up. These patients might not have been followed long enough to see whether they developed adverse events, thus systematically underestimating the adverse event rates. Therefore, we present the updated follow-up of that cohort reporting the results of patients, at least, 1 year from DEB use, both at the 1-year and total follow-up after DEB use (table 1). Table 1. Updated 1-year and overall follow-up n = 150 Death MI TLR LT MACE 1-year, % (n) 1.3 (2) 2.0 (3) 3.3 (5) 0 (0) 6.0 (9) All follow-up, % (n) 1.3 (2) 2.0 (3) 12.9 (10) 0 (0) 17.7 (14) The 1-year rate are crude estimates, all follow-up analysis are presented as per Kaplan-Meier analysis. LT, lesion thrombosis; MACE, major adverse cardiovascular events; MI, myocardial infarction; TLR, target lesion revascularization. The original study was approved by the local institutional review board. Furthermore, patients gave their prior...
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".