Characterization of Carotid Plaque Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The main objective of this study was to evaluate CT angiographic (CTA) features that are able to predict the presence of intraplaque hemorrhage (IPH) as defined by MR-IPH. METHODS: One hundred sixty-seven consecutive patients (mean age 69 years, SD 12.8; 58 females) underwent both MR-IPH and CTA within 3 weeks. MR-IPH, the gold standard, was performed at 1.5 T using a neurovascular phased-array coil as a coronal T1-weighted 3-dimensional fat-suppressed acquisition. CTA was performed using a 4-slice or a 64-slice CT machine and evaluated, blinded to MR-IPH findings, for carotid stenosis, plaque density, and plaque ulceration. Plaque density was defined as the mean attenuation of plaque at the site of maximum stenosis and 2 sections above and below. Plaque ulceration was defined as outpouching of contrast into the plaque at least 2 mm deep on any single plane. RESULTS: Prevalence of IPH increased at higher degrees of carotid stenosis. Mean CT plaque density was higher for plaques with MRI-defined IPH (47 Hounsfield units) compared with without IPH (43 Hounsfield units; P=0.02). However, significant overlap between distributions of plaque densities limited the value of mean plaque density for prediction of IPH. CTA plaque ulceration had high sensitivity (80.0% to 91.4%), specificity (93.0% to 92.3%), positive predictive value (72.0% to 71.8%), and negative predictive value (95.0% to 97.9%) for prediction of IPH. Interobserver agreement for presence/absence of CTA plaque ulceration was excellent (kappa=0.80). CONCLUSIONS: CTA plaque ulceration, but not mean CTA plaque density, was useful for prediction of IPH as defined by the MR-IPH technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".