P.055 Efficacy and safety of using standardized size of stents in patients with carotid artery stenosis
Bibliographic record
Abstract
Background: Carotid artery stenosis causes up to 20% of ischemic strokes. Stenting is used as an alternative to endarterectomy in symptomatic patients. Most centers customize each individual stenosis to a specific stent size. However, this process can be time consuming and costly while the relative benefit has not been well evaluated yet. We hypothesized that a ‘one-size-fits-all’ approach to carotid stenting results in non-inferior results to a customized approach. Methods: We conducted a descriptive retrospective cohort study on patients who underwent carotid artery stenting looking for peri- and post-procedural complications. The primary outcomes were periprocedural (within 24 hours) or post procedural (within 30 day) TIA, stroke, or death. The secondary outcome was the estimated degree of stenosis on follow up ultrasound performed within 6 months of the procedure. Results: The complication rate was 4.5%, 6.5% for 24 hours and 30 day post-procedure, respictively. Age and degree of stenosis on post procedural cerebral angiogram were associated with increased risk of complication. Severe restenosis or occlusion was reported in 16.8% of patients within 6 months post-procedure. Conclusions: Our study suggests that using a simplified, one-size-fits-all, approach to carotid stenting results in safe and effective outcomes, suggesting a route to possibly simplify a complex medical procedure.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".