The effect of rituximab on anti‐platelet autoantibody levels in patients with immune thrombocytopenia
Bibliographic record
Abstract
Rituximab is an effective therapy resulting in a platelet count improvement in 60% of patients with immune thrombocytopenia (ITP). Rituximab depletes B cells; thus, a reduction in platelet autoantibody levels would be anticipated in patients who achieve a clinical response to this treatment. The objectives of this study were to determine whether rituximab was associated with a reduction in platelet autoantibody levels, and to correlate the loss of autoantibodies with the achievement of a treatment response. We performed a case-control study nested within a previous randomized controlled trial of standard therapy plus adjuvant rituximab or placebo. We measured platelet-bound anti-glycoprotein (GP) IIbIIIa and anti-GPIbIX using the antigen capture test. Of 55 evaluable patients, 25 (45%) had a detectable platelet autoantibody at baseline. Rituximab was associated with a significant reduction in anti-GPIIbIIIa levels (P = 0·02) but not anti-GPIbIX levels (P = 0·51) compared with placebo. Neither the presence of an autoantibody at baseline nor the loss of the autoantibody after treatment was associated with a response to rituximab. The subset of patients with persistent autoantibodies after treatment failed to achieve a platelet count response, suggesting that persistence of platelet autoantibodies can be a marker of disease severity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".