Differentiating Carotid Free-Floating Thrombus From Atheromatous Plaque Using Intraluminal Filling Defect Length on CTA
Bibliographic record
Abstract
Objective To validate a previously proposed filling defect length threshold of >3.8 mm on CT angiography (CTA) to discriminate between free-floating thrombus (FFT) and plaque of atheroma. Methods This was a prospective multicenter observational study of 100 participants presenting with TIA/stroke symptoms and a carotid intraluminal filling defect on initial CTA. Follow-up CTA was obtained within 1 week and at weeks 2 and 4 if the intraluminal filling defect was unchanged in length. Resolution or decreased length was diagnostic of FFT, whereas its static appearance after 4 weeks was indicative of plaque. Diagnostic accuracy of FFT length was assessed by receiver operating characteristic analysis. Results Ninety-five participants (mean [SD] age 68 [13] years, 61 men, 83 participants with FFT, 12 participants with a plaque) were evaluated. The >3.8-mm threshold had a sensitivity of 88% (73 of 83) (95% confidence interval [CI] 78%–94%) and specificity of 83% (10 of 12) (95% CI 51%–97%) (area under the curve 0.91, p < 0.001) for the diagnosis of FFT. The optimal length threshold was >3.64 mm with a sensitivity of 89% (74 of 83) (95% CI 80%–95%) and specificity of 83% (10 of 12) (95% CI 51%–97%). Adjusted logistic regression showed that every 1-mm increase in intraluminal filling defect length is associated with an increase in odds of FFT of 4.6 (95% CI 1.9–11.1, p = 0.01). Conclusion CTA enables accurate differentiation of FFT vs plaque using craniocaudal length thresholds. Trial Registration Information ClinicalTrials.gov Identifier: NCT02405845. Classification of Evidence This study provides Class I evidence that in patients with TIA/stroke symptoms, the presence of CTA-identified filling defects of lengths >3.8 mm accurately discriminates FFT from atheromatous plaque.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".