Predicting Disease Activity in Systemic Vasculitides: On the Hunt for Potential Candidates
Bibliographic record
Abstract
Balancing the risk of disease recurrence and damage attributable to the prescribed immunosuppressive measures is one of the most important issues in the management of vasculitides in the 21st century. Several aspects may be taken into account to measure disease activity (i.e., imaging methods, disease activity scores, and biomarkers) potentially distinguishing between active and quiescent disease. There is ongoing debate over how such a biomarker is defined, but in the context of vasculitides, this definition may be most appropriate: “A biological observation that substitutes for and ideally predicts a clinically relevant endpoint or intermediate outcome that is more difficult to observe”1. While this is a general assumption, biomarker biology in systemic vasculitides is a complex issue. Several biomarker classes have been proposed, highlighting unmet needs in the management of these diseases: (1) at the time of diagnosis to predict remission (early assessment of treatment response) or prediction of relapse; (2) to stage the respective disease (either to replace invasive or improve existing techniques); (3) to assess current vasculitis activity (especially in those with mild disease activity and to rule out potential differential diagnoses, i.e., infections); and (4) to predict longterm prognosis (prediction of treatment response, relapse probability, damage attributable to the disease or therapy, and other outcomes)2. In this issue of The Journal , Rodriguez-Pla and colleagues3 analyzed a panel of biomarkers in 4 vasculitides: giant cell arteritis (GCA, 60 patients), Takayasu arteritis (TA, 29 patients), polyarteritis nodosa (PAN, 26 patients), and eosinophilic granulomatosis with polyangiitis (EGPA, 37 patients). The hypothesis-generating approach used by the authors included 22 biomarkers potentially involved in the pathogenesis of at least 1 of the studied diseases, subdivided into the following categories: cytokines, chemokines, soluble receptors, markers of microvascular damage, markers of tissue damage and repair. Further, C-reactive protein and … Address correspondence to Dr. A. Kronbichler, Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria. E-mail: andreas.kronbichler{at}i-med.ac.at
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 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".