Reflections by Contrarians on the Post-CREST Evaluation of Carotid Stenting for Stroke Prevention
Bibliographic record
Abstract
Carotid angioplasty and stenting has become a popular alternative to carotid endarterectomy for the treatment of carotid stenosis in stroke. Evidence from early randomized controlled trials comparing these interventions revealed mixed results. The largest such trial, the Carotid Revascularization Endarterectomy vs. Stenting Trial recently showed equivalence of the procedures in a mixed cohort of both symptomatic and asymptomatic patients. These results have been heralded in North America as definitively demonstrating the safety and efficacy of carotid angioplasty and stenting, making it an attractive alternative to carotid endarterectomy. It is therefore probable that many more asymptomatic patients will be subjected to Carotid angioplasty and stenting, perceived by many to be less invasive than carotid endarterectomy. The authors argue that the design of Carotid Revascularization Endarterectomy vs. Stenting Trial was flawed by the mixture of two dissimilar patient groups, thus violating the principle of ceteris paribus, essential for the validity of a randomized controlled trials. The evidence for any invasive treatment of asymptomatic carotid disease is weak, with recent data favouring purely medical management. The authors believe that carotid angioplasty and stenting in asymptomatic patients should cease until better evidence is available.
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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 | 0.073 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.040 | 0.047 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".