Abstract 155: Comparative Effectiveness of Carotid Endarterectomy Compared to Medical Therapy Among Patients With Asymptomatic Carotid Stenosis
Bibliographic record
Abstract
Background: Carotid endarterectomy (CEA) reduces stroke risk compared to medical therapy alone among patients with asymptomatic carotid stenosis. CEA involves a tradeoff between higher perioperative short-term risks in exchange for a lower long-term risk of stroke. However, overall declines in stroke rates raise concerns that CEA may no longer be a preferred treatment. We examined the effectiveness of CEA compared to medical therapy (MT) among asymptomatic patients in preventing stroke and stroke-death within 5 years of follow-up. Methods: We identified Veterans ≥65 years old with carotid stenosis (n=2712 CEA and n=2509 MT patients) who did not have a history of stroke or transient ischemic attack. We propensity score-matched MT patients to CEA patients to control for baseline confounding and used methods to mimic analyses from the Asymptomatic Carotid Stenosis Trial, the last published trial to compare CEA to MT. We accounted for “immortal time” bias by randomizing patients to CEA and MT groups and censoring patients if their actual treatment became inconsistent with the arm in which they were randomized (e.g., patient received CEA, but was randomized to MT). We accounted for the informative censoring by estimating time-dependent inverse probability of censoring weights using measured covariates (demographics and 72 time-varying comorbidities). We computed weighted Kaplan-Meier (KM) curves and estimated the risk of stroke/stroke-death in each group over 5 years of follow-up. Results: The observed stroke or death rate (perioperative complications) within 30 days in the CEA arm was 3%. The 5-year risk were similar among patients randomized to CEA 5.5% (95% CI, 4.3%-6.7%) versus MT 7.6% (95% CI,5.9%-9.2%) (risk difference, -2.1%, 95% CI -4%- 0%) with little difference in the KM curves (logrank p=0.2). Conclusion: CEA was not superior to MT in a community sample of Veterans after 5 years of follow-up, suggesting that CEA may no longer be the preferred treatment strategy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".