Histologically Verified Carotid Plaque Characteristics by Ultrasound: A Diagnostic Accuracy Systematic Review
Bibliographic record
Abstract
Ultrasound (US) has been considered the first-line diagnostic technique for evaluating carotid atherosclerosis, where plaque composition plays a key role in stroke risk. We aimed to analyze the diagnostic accuracy of carotid plaque characteristics using US techniques compared to histology in patients with symptomatic/asymptomatic carotid plaques. After prospective study registration in PROSPERO, we searched Medline Ovid, Embase.com, Cochrane Library, and Web of Science without any search limitation for the diagnostic accuracy of US in detecting carotid plaque features based on histology. From 8168 studies, 63 were included evaluating 13 histologically verified plaque characteristics by 14 different US techniques. Diagnostic accuracies for all plaque characteristics usually varied between 35% and 100% without a trend towards increasing accuracy over the last 40 y but were affected by large heterogeneity. In characteristics with >5 diagnostic accuracy comparisons, the highest diagnostic performance was found for detection of calcification (mean sensitivity 65.7%/mean specificity 84.7%), fibrous tissue (61.2%/84.9%), vulnerable/unstable plaque (76.3%/70.3%), and stable plaque (63.2%/82.7%). However, several advanced techniques investigated showed high diagnostic accuracy, promising interesting diagnostic options for the future. Carotid US allows for widely available and reliable evaluation of atherosclerotic plaque morphology by conventional and advanced techniques. 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 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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".