Predictors of Recurrent In‐Stent Restenosis after Beta‐Radiation: An Analysis from the START 40/20 Trial
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify potential predictors, including clinical, procedural, angiographic, and intravascular ultrasound (IVUS) parameters, for recurrent in-stent restenosis (ISR) following beta-radiation 90Strontium/Yttrium (90Sr/Y) in a large multicenter trial. BACKGROUND: Although adjunct brachytherapy reduces recurrent ISR after primary catheter-based intervention, recurrence of stenosis after brachytherapy still occurs. METHODS: We analyzed 185 IVUS cohort patients in the STent And Restenosis Therapy (START) 40/20 trial where a 40-mm, 90Sr/Y, radioactive source train was exclusively used for treatment of ISR to be treatable with a 20-mm balloon. RESULTS: Thirty-nine patients underwent target lesion revascularization. Preliminary univariate analysis showed that age, smoking, balloon/artery ratio, geographic miss, minimum lumen diameter, and diameter stenosis at baseline were associated with target lesion revascularization, while none of IVUS variables were (minimum lumen area, minimum stent area, or residual plaque burden). The multivariate logistic regression analysis showed that younger age, lower balloon/artery ratio, and presence of geographic miss were independent predictors of target lesion revascularization. CONCLUSIONS: Even with adjunct beta-radiation therapy, initial mechanical optimization, such as appropriate balloon sizing and positioning, may be critical for the prevention of recurrent ISR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".