Association of Vascular Physical Examination Findings and Arteriographic Lesions in Large Vessel Vasculitis
Bibliographic record
Abstract
OBJECTIVE: To assess the utility of the vascular physical examination to detect arteriographic lesions in patients with established large vessel vasculitis (LVV), including Takayasu's arteritis (TAK) and giant cell arteritis (GCA). METHODS: In total, 100 patients (TAK = 68, GCA = 32) underwent standardized physical examination and angiography of the carotid, subclavian, and axillary arteries. Sensitivity and specificity were calculated for the association between findings on physical examination focusing on the vascular system (absent pulse, bruit, and blood pressure difference) and arteriographic lesions defined as stenosis, occlusion, or aneurysm. RESULTS: We found 67% of patients had at least 1 abnormality on physical examination (74% TAK, 53% GCA). Arteriographic lesions were seen in 76% of patients (82% TAK, 63% GCA). Individual physical examination findings had poor sensitivity (range 14%-50%) and good-excellent specificity (range 71%-98%) to detect arteriographic lesions. Even when considering physical examination findings in combination, at least 30% of arteriographic lesions were missed. Specificity improved (range 88%-100%) if individual physical examination findings were compared to a broader region of vessels rather than specific anatomically correlated vessels and if ≥ 1 physical examination findings were combined. CONCLUSION: In patients with established LVV, physical examination alone is worthwhile to detect arterial disease but does not always localize or reveal the full extent of arteriographic lesions. Abnormal vascular system findings on physical examination are highly associated with the presence of arterial lesions, but normal findings on physical examination do not exclude the possibility of arterial disease. Serial angiographic assessment is advisable to monitor arterial disease in patients with established LVV.
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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 | 0.008 |
| 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) | 0.002 | 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".