Negative anti-neutrophil cytoplasm antibody at switch to maintenance therapy is associated with a reduced risk of relapse
Bibliographic record
Abstract
BACKGROUND: Relapse of disease is frequent in anti-neutrophil cytoplasm antibody (ANCA)-associated vasculitis (AAV). It is unclear whether persistent ANCA when starting maintenance therapy increases the risk of relapse. We examined the association between ANCA status and relapse in two randomised controlled trials. METHODS: ANCA-positive patients in two trials, CYCLOPS and IMPROVE, were switched from cyclophosphamide to maintenance therapy after achieving clinical remission. We classified patients as being either ANCA-positive or ANCA-negative at the time they started maintenance therapy. We compared the risk of relapse in ANCA-positive and ANCA-negative patients. RESULTS: Of 252 patients included, 102 (40%) experienced at least one relapse during the follow-up period. At the time of the switch from induction to maintenance therapy, 111 were ANCA-positive, of whom 55 (50%) relapsed, compared to 141 patients who were ANCA-negative, of whom 47 (33%) relapsed. In multivariable time-to-event analysis, a reduced risk of relapse was associated with having become ANCA-negative at the time of switching to maintenance therapy (hazard ratio 0.63, 95% confidence interval 0.42-0.95; p = 0.026). In addition, initial proteinase 3 (PR3)-ANCA, younger age, lower serum creatinine, pulsed cyclophosphamide for remission induction, and mycophenolate mofetil for remission maintenance were all associated with an increased risk of relapse. CONCLUSIONS: Becoming ANCA-negative before the switch to maintenance is associated with a reduced risk of relapse. TRIAL REGISTRATION: CYCLOPS: ClinicalTrials.gov, NCT00430105 . Registered retrospectively on 31 January 2007. IMPROVE: ClinicalTrials.gov, NCT00307645 . Registered retrospectively on 27 March 2006.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".