Childhood-onset Takayasu arteritis: clinical presentation, challenges and disease course
Bibliographic record
Abstract
BACKGROUND: Takayasu arteritis (TAK) is a rare granulomatous inflammatory vasculitis primarily affecting the aorta and its major branches. Data on childhood-onset TAK (c-TAK) remain scarce. This study retrospectively evaluates the clinical presentation, disease flares, treatment, and outcomes of c-TAK in a tertiary Canadian center. METHODS: We identified all children under 18 years of age at disease onset with a clinical diagnosis of TAK seen at Alberta Children's Hospital, Calgary, Canada, between 2000 and 2024. Patients meeting the EULAR/PRINTO/Pres classification criteria for c-TAK were included. Baseline demographic data, clinical presentation, laboratory findings, imaging results, disease flares, and treatment were documented. Additionally, we highlight two challenging cases due to their particularly complex disease trajectories. RESULTS: Six children (4 female) with a median age at diagnosis of 14.5 years (range: 4-17) met the classification criteria for c-TAK. Clinical presentation was variable, with the most common symptoms being fatigue (n = 4), weight loss (n = 3), and hypertension (n = 3). The most frequently affected arteries were the abdominal aorta and carotid arteries (n = 5) followed by ascending aorta (n = 4). All patients received corticosteroids for induction treatment. Additional immunosuppressive therapies included methotrexate (n = 5), infliximab (n = 2), tocilizumab (n = 2), IVIG (n = 2), etanercept (n = 1), adalimumab (n = 1), and cyclophosphamide (n = 1). CONCLUSIONS: TAK is a rare, potentially life-threatening large-vessel vasculitis. Early recognition is crucial for timely diagnosis and aggressive treatment initiation. Children with TAK often experience a complex disease course requiring multiple treatment adjustments and surgical or endovascular interventions. Large, multinational collaborations are essential for advancing our knowledge and improving patient outcomes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".