Improving the appropriateness of carotid endarterectomy
Bibliographic record
Abstract
Carotid endarterectomy (CE) has been used for stroke prevention since its introduction in 1954.1 It was embraced with great enthusiasm in the 1980s and rates soared.2 However, the lack of efficacy data from randomized controlled trials (RCTs) and doubts about its effectiveness had at least three effects. Rates began to drop, a major study of the appropriateness of CE was undertaken,3 and RCTs were conducted. The result today should be more appropriate and effective use of CE. The 1988 RAND study of the appropriateness of CE3 revealed a disturbing result; about a third of procedures were judged inappropriate, based upon the best evidence and expert opinion; a third were of uncertain value; and only a third were appropriate.3 This result tempered enthusiasm for CE and rates dropped.2 It also helped persuade the National Institute of Neurologic Disorders and Stroke to support the first major RCT of CE, the North American Symptomatic Carotid Endarterectomy Trial (NASCET).4 NASCET showed that CE substantially reduced the risk of stroke in symptomatic patients with ≥70% stenosis and to a lesser degree in those with 50 to 69% stenosis. A large European RCT substantiated these results for symptomatic patients.5 The Asymptomatic Carotid Stenosis Trial (ACAS)6 showed a more modest reduction of stroke in asymptomatic patients with ≥60% carotid stenosis. Significant …
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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 | 0.007 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".