The effect of in‐stent restenosis on hemodialysis access patency
Bibliographic record
Abstract
Endovascular stents have recently been shown to extend access patency in thrombosed and stenotic arteriovenous grafts (AVG). Concern remains over the frequency and severity of in-stent restenosis, though this has not been rigorously defined to date. The study was a retrospective analysis of hemodialysis patients referred for access dysfunction during a 2-year period. Using a prospectively collected, vascular access database, we identified 76 patients seen for follow-up angiography due to access dysfunction after stent placement. We compared the effect of in-stent restenosis vs. de novo lesions in patients with previously placed endovascular stents. Measured outcomes were primary assisted patency and frequency of in-stent and de novo lesions. Thirty-five (46.1%) patients had de novo lesions, while 41 (53.9%) had in-stent restenosis. In-stent restenosis was found to be the only factor associated with severity of luminal stenosis (beta=0.35, 95% confidence interval 2.21-15.48, P=0.01). In-stent restenosis was associated with increased primary patency among AVGs (hazards ratio 3.10; 95% confidence interval 1.35-7.10; P=0.008). Primary patency of in-stent restenosis vs. de novo lesions for AVGs were respectively: 78% vs. 94% at 1 month, 56% vs. 42% at 3 months, 33% vs. 6% at 6 months. For arteriovenous fistulae, the difference in primary patency of in-stent vs. de novo lesions was not statistically significant. In-stent restenosis is associated with higher percent luminal diameter lesions, while de novo lesions rather than in-stent restenosis are associated with higher risk of AVG access failure and reduced primary patency.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".