Follow-up Contrast-Enhanced Ultrasonography of the Carotid Artery in Patients With Takayasu Arteritis: A Retrospective Study
Bibliographic record
Abstract
Objective The literature describing follow-up carotid contrast-enhanced ultrasonography (CEUS) is limited. We report our experience with monitoring CEUS that is performed in patients with Takayasu arteritis (TAK). Methods We retrospectively analyzed patients with TAK who had undergone carotid CEUS 2 or more times with a follow-up duration of 12 or more months at Xijing Hospital between 2017 and 2020. We described how CEUS interpretation changed, and we recorded the state of remission (ie, bilateral CEUS visual grades ≤ 1) or relapse as determined by imaging. Results In total, 106 patients with TAK and 425 CEUS visits were included in the study; the median follow- up was 25 (IQR 18-30) months. The CEUS vascularization grade was significantly associated with the Kerr criteria (r = 0.532, P < 0.001), erythrocyte sedimentation rate (P < 0.001), and C-reactive protein level (P < 0.001). At baseline, 76 patients (71.7%) had active disease as determined by CEUS and 30 (28.3%) had inactive disease. The midterm assessment (median 13, IQR 10-16 months) showed that 29 out of 76 CEUS-active patients (38.2%) achieved complete response, 34 (44.7%) achieved partial response, and 13 (17.1%) did not respond. At the last visit, the total number of responders was 78 out of 94 (83.0%). CEUS relapse was observed in 28 out of 57 (49.1%) patients, with a median of 16 (IQR 10-21) months. The Kaplan-Meier curve demonstrated that the remission rate evaluated by the CEUS-combined method (median 22 months) was lower than that of the clinical-only evaluation (median 11 months; P < 0.001). Conclusion Response or relapse according to CEUS was detected in most patients during follow-up. CEUS is an effective technique for detecting carotid artery inflammation in patients with TAK.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".