Endarterectomy for Carotid Stenosis: New Approaches in Patient Selection
Bibliographic record
Abstract
Results of randomized trials on carotid endarterectomy make it mandatory that therapeutic decisions for patients with carotid stenosis consider the degree of stenosis, presence of symptoms, skill of surgeon and time since the last ischemic event. Patients with severe (>70% by angiogram) stenosis should receive carotid endarterectomy, provided the operative risk is <6% and symptoms have recurred within 6 months. With moderate stenosis (50--69% by angiogram), and with similar low operative risk and time limit, males with hemispheric, nondisabling stroke and appropriate CT lesion will benefit from carotid endarterectomy. Patients with TIA only, retinal symptoms alone and who are women are not going to benefit in this range of stenosis. Particularly at risk with medical care alone are symptomatic patients with coexistent intracranial stenosis, widespread white-matter lesions, intraluminal thrombi, contralateral occlusion and absence of good collateral circulation. The same high-risk patients, enjoy good long-term results from endarterectomy. Lacunar syndromes at presentation respond to endarterectomy, but with less benefit. Symptomatic patients do as well, regardless of age, provided patients with serious cardiac disorders and with organ failure are avoided. Serious doubt exists about indications for endarterectomy in asymptomatic subjects. Even if the upper limit of 3% perioperative risk is exceeded (and in large institutional databases and other studies, it usually is), the risk of large-artery strokes from the asymptomatic lesion is only slightly above the risk facing these subjects from lacunar and cardioembolic stroke. To prevent 1 large-artery stroke in 5 years in asymptomatic subjects requires that 111 subjects be submitted to endarterectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.171 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".