Fostamatinib for immune thrombocytopenic purpura in adult patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Immune thrombocytopenic purpura (ITP) is an immune disorder characterized by thrombocytopenia. Fostamatinib is an orally administered spleen tyrosine kinase inhibitor intended to treat refractory ITP. To evaluate the efficacy and safety of fostamatinib as a subsequent‐line therapy for ITP in adults. We searched four electronic databases for primary studies of any design. Primary efficacy outcomes included proportions of patients achieving overall (≥30 × 109 cells/L), partial (≥50 × 109 cells/L), and stable (as defined in original studies) platelet response. Safety outcomes included rescue medication use and other adverse events. We used narrative synthesis and Mantel–Haenszel random effect meta‐analysis to summarize results. Our systematic review included 11 studies for analyses (n = 722). Weighted mean proportions of patients achieving overall, partial, and stable responses with fostamatinib treatment were 0.70 [0.62, 0.76], 0.48 [0.36, 0.61], and 0.28 [0.16, 0.44], respectively. Fostamatinib was favored over placebo for partial (relative risk [RR] = 3.04, 95% confidence interval [CI] [1.53, 6.06]) and stable (RR = 6.43, 95% CI [1.58, 26.23]) responses. Patients on fostamatinib required less rescue medication and were more likely to experience hypertension. Fostamatinib is a viable subsequent‐line therapy option for refractory ITP. Given the heterogeneous data and large number of small studies, these results should be interpreted cautiously.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".