Contrast-Enhanced Magnetic Resonance Angiography of Carotid Arteries
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Contrast-enhanced magnetic resonance angiography (CEMRA) is among the newer noninvasive tests used for the evaluation of patients with carotid artery disease. Evidence supporting its utility in routine clinical practice is lacking. METHODS: The results of CEMRA were compared with those of catheter angiography in 50 consecutive patients being evaluated for carotid endarterectomy (CEA) at a community hospital. Using indications for CEA based on published guidelines, we determined the rate of misclassification for surgery, sensitivity, specificity, and positive and negative predictive values. In addition, the interrater agreement (kappa score) of CEMRA was compared with that of catheter angiography in the studied population and with interpretations provided by 2 blinded radiologists. RESULTS: Compared with catheter angiography, 24% (95% CI, 12% to 36%) of patients would have been misclassified for CEA on the basis of CEMRA results alone. CEMRA was associated with sensitivity of 92%, specificity of 62%, positive predictive value of 78%, and negative predictive value of 89%. When both CEMRA and duplex Doppler ultrasound were performed and the results were concordant, the misclassification rate decreased to 17% (95% CI, 2% to 32%). kappa scores were similar for CEMRA and catheter angiography (0.72 and 0.75, respectively). CONCLUSIONS: CEMRA was found to be highly sensitive for detection of surgically amenable carotid stenosis. kappa scores for the interpretation of CEMRA and catheter angiography were similar. However, clinicians should be cautious when using CEMRA alone for surgical decision making in CEA candidates because a significant number of patients may be misclassified. The rate of misclassification is reduced when the results of CEMRA and duplex Doppler ultrasound are concordant.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".