Carotid Intima-media Thickness/Diameter Ratio and Peak Systolic Velocity as Risk Factors for Neurological Severe Ischemic Events in Takayasu Arteritis
Bibliographic record
Abstract
Objective To characterize Takayasu arteritis (TA) with supra-aortic involvement and determine the associations between clinical features, carotid ultrasonographic (US) variables, and neurological severe ischemic events (SIEs). Methods Patients with supra-aortic involvement including brachiocephalic trunk, bilateral common carotid artery and internal carotid artery, and bilateral subclavian and vertebral artery and baseline carotid US examination were enrolled from the East China TA cohort. Bilateral carotid diameter, intima-media thickness (IMT), and peak systolic velocity (PSV) were measured by US. Then, the IMT/diameter ratio (IDR) was calculated. Risk factors associated with neurological SIEs were analyzed by multivariate logistic regression. Results In total, 295 patients were included, of whom 260 (88.14%) were female, and 93 (31.53%) experienced neurological SIEs. Involved supra-aortic artery distribution ( P = 0.04) and number ( P < 0.01) differed between subjects with neurologic and nonneurologic SIEs, showing higher prevalence of common carotid and vertebral artery involvement after Bonferroni correction and 56.99% patients having ≥ 4 involved arteries in the neurological SIE group. The bilateral IDR ( P < 0.01) differed between patients with and without neurological SIEs. The carotid IDR (left: cut-off value ≥ 0.55, OR 2.75, 95% CI 1.24–6.07, P = 0.01; right: ≥ 0.58, OR 2.70, 95% CI 1.21–6.02, P = 0.01) and left carotid PSV (≤ 76.00 cm/s, OR 3.09, 95% CI 1.53–6.27, P < 0.01), as well as involved supra-aortic artery number (≥ 4, OR 2.33, 95% CI 1.15–4.72, P = 0.02) were independently associated with neurological SIEs. Conclusion The carotid IDR and PSV might be performed as valuable markers for recognizing neurological SIEs in patients with TA with supra-aortic lesions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".