Effect of Treatment on Imaging, Clinical, and Serologic Assessments of Disease Activity in Large-vessel Vasculitis
Bibliographic record
Abstract
OBJECTIVE: Disease activity in large-vessel vasculitis (LVV) is traditionally assessed by clinical and serological variables rather than vascular imaging. This study determined the effect of treatment on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) vascular activity in relation to clinical- and serologic-based assessments. METHODS: Patients with giant cell arteritis (GCA) or Takayasu arteritis (TA) were prospectively evaluated at 6-month intervals in an observational cohort. Treatment changes were made at least 3 months before the followup visit and categorized as increased, decreased, or unchanged. Imaging (FDG-PET qualitative analysis), clinical, and serologic (erythrocyte sedimentation rate, C-reactive protein) assessments were determined at each visit and compared over interval visits. RESULTS: Serial assessments were performed in 52 patients with LVV (GCA = 31; TA = 21) over 156 visits. Increased, decreased, or unchanged therapy was recorded for 36-, 23-, and 32-visit intervals, respectively. When treatment was increased, there was significant reduction in disease activity by imaging, clinical, and inflammatory markers (p ≤ 0.01 for each). When treatment was unchanged, all 3 assessments of disease activity remained similarly unchanged over 6-month intervals. When treatment was reduced, PET activity significantly worsened (p = 0.02) but clinical and serologic activity did not significantly change. Treatment of GCA with tocilizumab and of TA with tumor necrosis factor inhibitors resulted in significant improvement in imaging and clinical assessments of disease activity, but only rarely did the assessments both become normal. CONCLUSION: In addition to clinical and serologic assessments, vascular imaging has potential to monitor disease activity in LVV and should be tested as an outcome measure in randomized clinical trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".