Ongoing Late Lumen Loss with the CYPHER and TAXUS Drug-eluting Stents Supports a Theory of Catch-up Restenosis
Bibliographic record
Abstract
Luminal loss and restenosis are critical problems in coronary artery drug-eluting stents (DES). These implants need to minimise long-term neointimal coverage and maximise blood flow. Two studies compared the effects of different types of DES on lumen loss after surgical implantation. The first study included 2,030 patients and showed that over two years late luminal loss (termed ‘late luminal creep’) progressed for two types of commercially prepared permanent polymer stents containing rapamycin or paclitaxel (CYPHER and TAXUS), but not for a polymer-free in-house coated stent containing rapamycin (YUKON). In a smaller study, luminal coverage was lower with the CYPHER than with the YUKON stent, but struts of the stent structure were better covered with the YUKON stent and less likely to cause an obstruction. Stents should ideally limit luminal loss but also allow for sufficient coverage to prevent thrombotic hazards.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".