Interleukin 6 Targeting in Refractory Takayasu Arteritis: Serial Noninvasive Imaging is Mandatory to Monitor Efficacy
Bibliographic record
Abstract
Takayasu arteritis (TA) is a true orphan disease. There are no published controlled clinical trials and the evidence on which to develop management strategies and therapeutic guidelines is rudimentary, particularly when compared to the advances seen in other vasculitides and inflammatory rheumatic diseases1. Additional significant challenges to more effective management exist. These include a lack of awareness of the disease and a resultant delay in diagnosis, as well as the absence of gold standards for the quantification of disease activity and imaging2. However, the outlook is beginning to change for the better, with early evidence pointing towards improved outcomes for patients3. This improvement reflects earlier use of combined immunosuppression and the increased availability of noninvasive imaging modalities, particularly 18F-fluorodeoxyglucose positron emission computerized tomography (18F-FDG-CT-PET), high-resolution ultrasound (US), magnetic resonance, and CT angiography (MRA and CTA). Perhaps in light of the paucity of clinical trial evidence, corticosteroids remain the mainstay of therapy and achieve remission in 60% of cases4. However, when tapered, most patients relapse. Our practice is to initiate combination immunosuppressive therapy at diagnosis to optimize control of disease activity and steroid-sparing2. Small open-label studies support the efficacy of methotrexate, azathioprine, and mycophenolate5. Although cyclophosphamide is typically reserved for refractory or life-threatening disease, we have some concerns about its efficacy in TA, and its use is often contraindicated due to a need to preserve fertility in this young, predominantly female, patient group. Despite optimal combination immunosuppression, up to 30% of patients fail to respond adequately. This observation led to the investigation of biologic therapies already available for other rheumatic diseases. Nearly a decade ago Hoffman and colleagues reported the initial open-label study demonstrating the efficacy of tumor necrosis factor-α (TNF-α) blockade in TA6. Since … Address correspondence to Professor Mason; Vascular Sciences, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK. E-mail: justin.mason{at}imperial.ac.uk
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".