Rituximab versus azathioprine for ANCA-associated vasculitis maintenance therapy: impact on global disability and health-related quality of life.
Bibliographic record
Abstract
OBJECTIVES: To investigate the effects on health-related quality of life (HRQOL) and functional capability of rituximab vs azathioprine for ANCA-associated vasculitis (AAV) maintenance therapy. METHODS: In a 24-month phase III randomised-controlled trial, 115 patients over time received rituximab or azathioprine for AAV maintenance therapy. Mean changes of 36-item Short-form Health Survey (SF-36) and Health Assessment Questionnaire (HAQ) scores from baseline were analysed. RESULTS: Mean improvements of HAQ scores, from baseline to month 24 were significantly better for the rituximab (0.16 points lower) than the azathioprine group (p=0.038). As demonstrated by SF-36, study patients' baseline HRQOL was significantly impaired compared with age- and sex-matched US norms. At month 24, mean changes from baseline of SF-36 physical component score tended to be better for the rituximab group (+3.95 points, p=0.067) whereas mean changes from baseline of the SF-36 mental component score were significantly better for the azathioprine group (+4.23 points, p=0.041). CONCLUSIONS: Azathioprine-treated patients' for AAV maintenance therapy showed a decline in physical abilities when compared to RTX at M24 in the MAINRITSAN trial. TRIAL REGISTRATION: ClinicalTrials.gov, http://clinicaltrials.gov/, NCT00748644.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".