Internal Carotid Artery Stenosis Measurement
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Clinical trials have shown that carotid endarterectomy reduces stroke risk in symptomatic individuals with severe internal carotid artery (ICA) stenosis. As a result of these trials, digital subtraction angiography (DSA) became a standard of reference for ICA stenosis diagnosis. Newer 3D techniques provide a larger number of views than DSA, which may influence the estimated degree of stenosis. We evaluate this possibility by directly comparing stenosis grades from 3D computed rotational angiography (CRA) and DSA. METHODS: As a prospective diagnostic study, we performed CRA and DSA on 26 consecutive symptomatic patients. Only 1 angiographic procedure was performed on normal asymptomatic arteries, yielding 42 arteries for comparison. Four neuroradiologists graded the CRA maximum intensity projections (MIPs) and DSA images, according to the North American Symptomatic Carotid Endarterectomy Trial guidelines. CRA studies included a search for the narrowest view by evaluating 60 MIPs generated at 3 degrees intervals and measurement of actual artery diameters. Artery diameters and stenosis grades were analyzed graphically; statistical significance was determined using a paired t test. RESULTS: The mean difference of 1.2% (CI, -18%, 21%) between CRA and DSA stenosis grades was not statistically significant (P=0.55). Agreement of the optimal CRA viewing angle was limited, with an interobserver variability of 24+/-13 degrees . The interobserver variability of DSA and CRA stenosis grades, 9.1% (CI, 0%, 21%) and 9.4% (CI, 0%, 22%), respectively, was not significantly different (P=0.79). CONCLUSIONS: CRA provides stenosis grades equivalent to DSA, as well as absolute measurements, providing a comparison for newer 3D techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".