Variations in the clinical practice of physicians managing Takayasu arteritis: a nationwide survey
Bibliographic record
Abstract
OBJECTIVE: Takayasu arteritis (TAK) is a large vessel vasculitis that predominately affects young women and can cause severe ischemic complications. Given the rarity of TAK, the management of this condition is challenging. We aim to describe current rheumatologist practices for the management of TAK and identify discrepancies and gaps in knowledge. METHODS: An online survey (developed by the Canadian Vasculitis Network and approved by the Canadian Rheumatology Association) containing 48 questions with regard to the diagnosis, monitoring and treatment of TAK was distributed to 495 Canadian adult and pediatric rheu-matologists by email. RESULTS: Sixty-six rheumatologists completed the survey (13% response rate): the majority (73%) were from academic centers and ≤25% reported managing more than ten patients in their career. For establishing the diagnosis of TAK, they relied on a combination of signs and symptoms of ischemia, elevations of inflammatory markers and vascular imaging (typically computed tomography and magnetic resonance angiography). The frequency of monitoring for disease activity and the methods employed (clinical, laboratory or imaging) were variable. All physicians used corticosteroids for the treatment of TAK, but 42% would treat for at least 6-12 months, 26% for 12-24 months and 23% would never stop corticosteroids. Fifty-three percent would always use an immunosuppressant (most commonly methotrexate or azathioprine) in addition to corticosteroids and the remainder would only start an immunosuppressant in patients with refractory or relapsing disease. CONCLUSION: Physician practices for the management of TAK are variable, suggesting that there are knowledge gaps, which may impact outcomes 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".