B-Lymphocyte Depletion With Rituximab and β-Cell Function: Two-Year Results
Bibliographic record
Abstract
OBJECTIVE: We previously reported that selective depletion of B-lymphocytes with rituximab, an anti-CD20 monoclonal antibody, slowed decline of β-cell function in recent-onset type 1 diabetes mellitus (T1DM) at 1 year. Subjects were followed further to determine whether there was persistence of effect. RESEARCH DESIGN AND METHODS: Eighty-seven subjects (aged 8-40 years) were randomly assigned to, and 81 received, infusions of rituximab or placebo on days 1, 8, 15, and 22. The primary outcome-baseline-adjusted mean 2-h area under the curve (AUC) serum C-peptide during a mixed-meal tolerance test (MMTT) at 1 year-showed higher C-peptide AUC with rituximab versus placebo. Subjects were further followed with additional MMTTs every 6 months. RESULTS: The rate of decline of C-peptide was parallel between groups but shifted by 8.2 months in rituximab-treated subjects. Over 30 months, AUC, insulin dose, and HbA1c were similar for rituximab and placebo. However, in evaluating change in C-peptide over the entire follow-up period, the rituximab group means were significantly larger as compared within assessment times with the placebo group means using a global test (P = 0.03). Odds ratio for loss of C-peptide to <0.2 nmol/L following rituximab was 0.565 (P = 0.064). B-lymphocytes recovered to baseline values by 18 months. Serum IgG levels were maintained in the normal range but IgM levels were depressed. CONCLUSIONS: Like several other immunotherapeutic approaches tested, in recent-onset T1DM, rituximab delays the fall in C-peptide but does not appear to fundamentally alter the underlying pathophysiology of the disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".