Reducing the initial number of rituximab maintenance-therapy infusions for ANCA-associated vasculitides: randomized-trial post-hoc analysis
Bibliographic record
Abstract
OBJECTIVE: The randomized, controlled MAINRITSAN2 trial was designed to compare the capacity of an individually tailored therapy [randomization day 0 (D0)], with reinfusion only when CD19+ lymphocytes or ANCA had reappeared, or if the latter's titre rose markedly, with that of five fixed-schedule 500-mg rituximab infusions [D0 + D14, then months (M) 6, 12 and 18] to maintain ANCA-associated vasculitis (AAV) remissions. Relapse rates did not differ at M28. This ancillary study was undertaken to evaluate the effect of omitting the D14 rituximab infusion on AAV relapse rates at M12. METHODS: MAINRITSAN2 trial data were subjected to post-hoc analyses of M3, M6, M9 and M12 relapse-free survival rates in each arm as primary end points. Exploratory subgroup analyses were run according to CYC or rituximab induction and newly diagnosed or relapsing AAV. RESULTS: At M3, M6, M9 and M12, respectively, among the 161 patients included, 79/80 (98.8%), 76/80 (95%), 74/80 (92.5%) and 73/80 (91.3%) from D0, and 80/81 (98.8%), 78/81 (96.3%), 76/81 (93.8%) and 76/81 (93.8%) from D0+D14 groups were alive and relapse-free. No between-group differences were observed. Results were not affected by CYC or rituximab induction, or newly diagnosed or relapsing AAV. CONCLUSIONS: We were not able to detect a difference between the relapse-free survival rates for up to M12 for the D0 and D0+D14 rituximab-infusion groups, which could suggest that omitting the D14 rituximab remission-maintenance dose did not modify the short-term relapse-free rate. Nevertheless, results at M12 may also have been influenced by the rituximab-infusion strategies for both groups.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".