Carotid Dissection: Technical Factors Affecting Endovascular Therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To delineate factors associated with the successful endovascular treatment of extracranial carotid dissections, the authors review their management of 13 cases. METHODS: The records of 12 patients with 13 dissections were assessed with reference to mechanism of dissection, preoperative symptoms, presence of a pseudoaneurysm, treatment success, and etiology of treatment failure. Patients were followed prospectively and included six men and six women, ranging in age from 27 to 62 years. RESULTS: Angioplasty and stenting were performed successfully in 11 of 13 procedures (10 of 12 patients). Follow-up in these 10 patients demonstrated excellent patency through the stented segment in nine of the 11 treated vessels. Two patients, both of whom suffered their original dissection as a result of endarterectomy, required further angioplasty and stenting for stenosis outside the previously treated arterial segment. Regarding the treatment failures, a stent deployment device could not navigate a tortuous loop in one, while a microwire could not be advanced beyond a pseudoaneurysm in the second. Six patients had pseudoaneurysms, four of which were treated only with stenting across the dissected arterial segment. All pseudoaneurysms treated in this fashion resolved. No permanent complications occurred as a result of endovascular therapy. CONCLUSIONS: Angioplasty and stenting can be performed safely to manage carotid dissection. A pseudoaneurysm or tortuous anatomy can preclude therapy although the former typically resolves if angioplasty and stenting are feasible. Dissections secondary to endarterectomy may be associated with a higher rate of restenosis after stenting and may require further treatment.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".