Contrast-enhanced Ultrasonography for Monitoring Arterial Inflammation in Takayasu Arteritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the utility of contrast-enhanced ultrasound (CEUS) compared with 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in assessing vessel inflammation of Takayasu arteritis (TA). METHODS: This is a retrospective analysis of 71 patients with TA who had undergone carotid CEUS. Twenty-two of 71 patients underwent FDG-PET after CEUS. Clinical disease activity was assessed by Kerr criteria and the Indian Takayasu Clinical Activity Score 2010 (ITAS2010). We investigated the correlation between carotid vascularization on CEUS and clinical data. The consistency of carotid CEUS and PET data has been analyzed for TA disease activity. RESULTS: There was a statistically significant correlation between the results of CEUS and ITAS2010 (p = 0.004) or Kerr criteria (p < 0.001). According to ITAS2010, thirty-four of 71 patients with TA were clinically inactive. Assessment of 34 TA patients with clinically inactive disease yielded 11 CEUS scans that showed active lesions (visual grade ≥ 2) in the left or right carotid artery. In 22 cases that underwent CEUS and FDG-PET, 12 were active and 10 were inactive on the basis of ITAS2010. Moreover, bilateral carotid CEUS vascularization score positively correlated with vascular FDG uptake in these patients with TA (p = 0.004). When vascular inflammation was defined as FDG uptake with visual grade ≥ 2, carotid CEUS showed sensitivity of 100% and specificity of 80%. CONCLUSION: For TA patients with clinically inactive disease, CEUS could help clinicians to identify active lesions in the carotid vascular region. Carotid CEUS may be a rapid and cost-effective imaging tool in the followup of patients with TA.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".