Evaluation of carotid artery stenosis: contrast-enhanced magnetic resonance angiography compared with conventional digital subtraction angiography.
Bibliographic record
Abstract
OBJECTIVE: To compare retrospectively high-resolution contrast-enhanced magnetic resonance angiography with centric k-space filling and digital subtraction angiography in the assessment of degree of carotid artery stenosis. METHODS: Two neuroradiologists independently evaluated 14 carotid arteries (in 7 patients), and the degree of carotid stenosis determined was compared to calculate interobserver reliability. Thirty-six carotid arteries (in 18 patients) were then assessed with both contrast-enhanced magnetic resonance angiography and digital subtraction angiography. The degree of stenosis was determined by consensus, and the findings of the 2 modalities were compared. RESULTS: Close interobserver agreement was found with an intraclass correlation coefficient of 0.98 (95% confidence interval 0.95-0.99). Sensitivity of 86% and specificity of 91% were found for contrast-enhanced magnetic resonance angiography detection of surgically significant carotid stenosis compared with digital subtraction angiography. The Spearman rank correlation test also found a significant correlation (R(s) = 0.90, p < 0.001) in the comparison of the classifications of degree of carotid artery stenosis. CONCLUSION: Contrast-enhanced magnetic resonance angiography shows excellent correlation with digital subtraction angiography for the evaluation of carotid artery disease and has the potential to replace it in the assessment of degree of carotid artery stenosis.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".