Comparability of patients with ANCA-associated vasculitis enrolled in clinical trials or in observational cohorts.
Bibliographic record
Abstract
OBJECTIVES: To analyse the differences between patients with granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA) entered into randomised clinical trials (RCTs) and those followed in large observational cohorts. METHODS: The main characteristics and outcomes of patients with generalised and/or severe GPA or MPA with a five-factor score ≥ 1 enrolled in the French Vasculitis Study Group (FVSG) or the US-Canadian-based Vasculitis Clinical Research Consortium cohorts were compared to those enrolled in one of 2 FVSG clinical RCTs (WEG91, WEGENT) or 3 European Vasculitis Society clinical trials (CYCLOPS, CYCAZAREM, IMPROVE). RESULTS: 657 patients (65.3% with GPA) in RCTs were compared to 437 in cohorts (90.6% with GPA). RCT patients were older at diagnosis than the cohort patients (56.6 ± 13.9 vs. 46.8 ± 17.3 years), had higher Birmingham vasculitis activity score (19.5 ± 9.1 vs. 16.9 ± 7.4), and more frequent kidney disease (84.0% vs. 54.9%) but fewer ear, nose, and throat symptoms (56.8% vs. 72.2%). At 56 months post-diagnosis, mortality and relapse rates, adjusted for age and renal function, were higher for patients with GPA in RCTs vs. cohorts (10.7% vs. 2.5% [p=0.001] and 22.5% vs. 15.6% [p=0.03], respectively) but similar for patients with MPA (6.2% vs. 6.6% [p=0.92] and 16.6% vs. 10.1% [p=0.39], respectively). CONCLUSIONS: Patients with GPA or MPA in RCTs and those in observational cohorts show important differences that should be remembered when interpreting results based on these study populations.
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 | 0.051 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".