Bibliographic record
Abstract
Current treatment regimens for haematological autoimmune diseases are relatively non-selective and are often associated with considerable toxicity. Recently, it has become clear that B cells play a key role in both the development and perpetuation of autoimmunity, suggesting that B-cell depletion could be a valuable treatment approach for patients with autoimmune diseases. This article reviews data supporting the use of rituximab--an anti-CD20 monoclonal antibody that specifically depletes B cells--in four key autoimmune haematological disorders: idiopathic thrombocytopenic purpura (ITP); autoimmune haemolytic anaemia (AIHA); acquired haemophilia; and thrombotic thrombocytopenic purpura (TTP). Although treatment of ITP, AIHA, acquired haemophilia and TTP with rituximab is still relatively uncommon, results from case series and small phase II trials indicate that patients of all ages can respond to rituximab, irrespective of the number or type of prior treatments that they have received. Moreover, patients with these diseases receiving rituximab experienced predominantly mild adverse events, with only a few serious adverse events reported. These data suggest that rituximab provides an effective and well-tolerated alternative treatment option for patients with ITP, AIHA, acquired haemophilia and TTP, many of whom have limited treatment choices.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".