B-Lymphocyte Depletion With Rituximab and β-Cell Function: Two-Year Results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it