Response to steroids predicts response to rituximab in pediatric chronic immune thrombocytopenia
Bibliographic record
Abstract
BACKGROUND: Treatment choice in pediatric immune thrombocytopenia (ITP) is arbitrary, because few studies are powered to identify predictors of therapy response. Increasingly, rituximab is becoming a treatment of choice in those refractory to other therapies. METHODS: The objective of this study was to evaluate univariate and multivariable predictors of platelet count response to rituximab. After local IRB approval, 565 patients with chronic ITP enrolled and met criteria for this study in the longitudinal, North American Chronic ITP Registry (NACIR) between January 2004 and October 2010. Treatment response was defined as a post-treatment platelet count ≥ 50,000/µl within 16 weeks of rituximab and 14 days of steroids. Treatment response data were captured both retrospectively at enrollment and then prospectively. RESULTS: Eighty (14.2%) patients were treated with rituximab with an overall response rate of 63.8% (51/80). Univariate correlates of response to rituximab included the presence of secondary ITP and a positive response to steroids. In multivariable analysis, response to steroids remained a strong correlate of response to rituximab, OR 6.8 (95% CI 2.0-23.0, P = 0.002). Secondary ITP also remained a strong predictor of response to rituximab, OR 5.6 (95% CI 1.1-28.6, P = 0.04). Although 87.5% of patients who responded to steroids responded to rituximab, 48% with a negative response to steroids did respond to rituximab. CONCLUSION: In the NACIR, response to steroids and presence of secondary ITP were strong correlates of response to rituximab, a finding not previously reported in children or adults.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".