CASSH Registry: Rationale and Study Design
Bibliographic record
Abstract
Background: Carotid artery disease, a major cause of strokes, often results from carotid atherosclerotic stenosis. Although carotid endarterectomy has long been the standard treatment, carotid artery stenting (CAS) emerged as an alternative for high-risk surgical patients. Operator experience plays a crucial role in reducing complications, with neurointerventional specialists demonstrating proficiency in CAS. However, they are often underrepresented in CAS studies. The CASSH (Carotid Artery Stenting Outcomes in Comprehensive Stroke Hospitals) registry aims to provide comprehensive insights into CAS outcomes, particularly when performed by neurointerventionalists at comprehensive stroke centers. Methods: The CASSH is a multicenter, prospective, observational study currently enrolling patients with carotid artery stenosis undergoing CAS performed by neurointerventional physicians. All the participating sites will screen and report cases that meet inclusion criteria, on a monthly basis. The decision of whether to use CAS is at the discretion of the interventionalist. Results: We will collect patients' baseline clinical, demographic, and radiographic data. In addition, we plan to collect procedure variables and postoperative clinical and imaging data. Outcomes include the rate of postoperative symptomatic stroke (hemorrhagic/ischemic), access site complications, in-stent thrombosis, and mortality. Conclusion: Current literature underrepresents neurointerventionalists in CAS studies, especially as it pertains to procedural expertise and outcomes. CASSH is a prospective observational study that will enhance our understanding of CAS management and outcomes, emphasizing the benefits of neurointerventional expertise within comprehensive stroke centers.
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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 | 0.044 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".