Childhood Takayasu arteritis: disease course and response to therapy
Bibliographic record
Abstract
BACKGROUND: Takayasu arteritis (TAK) is a large vessel vasculitis that rarely affects children. Data on childhood TAK are scarce. The aim of this study was to analyze the presenting features, course and outcome of children with TAK, compare efficacy of treatment regimens and identify high-risk factors for adverse outcome. METHODS: A single-center cohort study of consecutive children fulfilling the EULAR/PRINTO/PReS criteria for childhood TAK between 1986 and 2015 was performed. Clinical phenotypes, laboratory markers, imaging features, disease course and treatment were documented. Disease activity was assessed using the Pediatric Vasculitis Disease Activity Score at each visit. OUTCOME: disease flare defined as new symptoms and/or increased inflammatory markers necessitating therapy escalation and/or new angiographic lesions, or death. ANALYSIS: logistic regression tested relevant variables for flare. Kaplan-Meier analyses compared treatment regimens. RESULTS: Twenty-seven children were included; 74% were female, median age at diagnosis was 12.4 years. Twenty-two (81%) children presented with active disease at diagnosis. Treatment regimens included corticosteroids alone (15%), corticosteroids plus methotrexate (37%), cyclophosphamide (19%), or a biologic agent (11%). Adverse outcomes were documented in 14/27 (52%) children: two (7%) died within 6 months of diagnosis, and 13 (48%) experienced disease flares. The 2-year flare-free survival was 80% with biologic treatments compared to 43% in non-biologic therapies (p = 0.03); at last follow-up, biologic therapies resulted in significantly higher rates of inactive disease (p = 0.02). No additional outcome predictor was identified. CONCLUSIONS: Childhood TAK carries a high disease burden; half of the children experienced flares and 7% died. Biologic therapies were associated with better control of disease activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".