Immunologic Reconstitution After Rituximab in Systemic Lupus Erythematosus: Why Should We Care?
Bibliographic record
Abstract
There is great interest in the role of B cells in autoimmune inflammatory diseases, running the spectrum from traditionally viewed B cell-centric diseases such as systemic lupus erythematosus (SLE) to rheumatoid arthritis (RA) to conventionally viewed T cell-mediated conditions such as multiple sclerosis. Although there is controversy regarding the place of B cell depletion in the SLE treatment armamentarium given the failure of 2 recent placebo-controlled trials (EXPLORER and LUNAR)1, this therapy is still used in the rheumatology community, particularly for refractory disease. Given the variability in response, I would argue that it is even more critical to understand how B cell depletion is efficacious and whether there are subsets of patients who will respond particularly well to B cell approaches as opposed to other treatment modalities. The article by Iwata and colleagues2 in this issue of The Journal examines changes in peripheral blood B and T cells longitudinally in 10 patients with active SLE treated with rituximab and attempts to correlate these changes with clinical response and relapse. A central finding is that prolonged remissions (in 8/10 patients) are associated with prolonged reductions in the fractions of both memory B and T cells, as well as downregulation of activation markers, including CD80 on B cells and CD40L, CD69, and inducible costimulator (ICOS) on T cells. An unfortunate omission is the absence of flow cytometry analysis in the 2 nonresponders. Other limitations of their study include the small numbers of patients studied, the limited T cell analyses (no T regulatory or T helper cell data), and incomplete definition of B cell subsets, with a notable lack of … Address correspondence to Dr. Anolik. E-mail: jennifer_anolik{at}urmc.rochester.edu
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".