Carotid plaque characteristics by computed Tomography: A diagnostic accuracy systematic review
Bibliographic record
Abstract
Beyond the stenosis degree, the carotid plaque morphology assessed by computed tomography may improve the stroke risk stratification and is recommended to be considered before interventional treatment according to current guidelines. This study aimed to systematically review the accuracy of computed tomography (CT) to detect carotid plaque characteristics compared to histology in patients with symptomatic and asymptomatic carotid plaques. We registered the protocol in PROSPERO and searched Medline Ovid, Embase.com , Cochrane Library, and Web of Science for diagnostic accuracy of CT in specific carotid plaque characteristic imaging compared to histology, without any search limitation up to May 27, 2022. Out of 8,168 studies, 20 studies that evaluated seven specific plaque characteristics were included in our systematic review. The best diagnostic performance was found for the detection of ulceration (sensitivity range 39.4–100% [mean 79.6%], specificity range 74–100% [mean 93.6%]), followed by calcification (72.7–100% [88.1%], 35.7–100% [80.1%]), lipid-rich necrotic core (63.2–95.6% [81.1%], 60–100% [80.1%]), and intraplaque hemorrhage (61.5–100% [86%], 20–99.5% [67.8%]). Only a few studies evaluated specifically vulnerable, mixed, and fibrous plaque. Diagnostic studies with larger sample sizes are needed, using novel available CT techniques that enable increasing diagnostic performance and decreasing radiation and amount of contrast agent. CT allows for highly accurate detection of carotid plaque features, particularly ulceration and calcification. These results underline the role of routine CT examinations to assess not only stenosis degree but also plaque morphology and individual patient stroke risk to better guide management. Registration: PROSPERO ID CRD42022329690 ( https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329690 ).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".