Second-Line Therapy for Immune Thrombocytopenia: Real-World Experience in Canada
Bibliographic record
Abstract
Background The sequence of second-line therapy used for the treatment of immune thrombocytopenia (ITP) is variable. This study aimed to describe the types and sequences of second-line therapies for a large cohort of ITP patients in Canada. Methods We completed a retrospective cohort study of the McMaster ITP Registry. We included patients with primary or secondary ITP who had received one or more second-line therapies including any of the splenectomy, rituximab, danazol, dapsone, or thrombopoietin receptor agonists (TPO-RAs), or immunosuppressant medications. Immunosuppressant medications included azathioprine, cyclophosphamide, cyclosporine, or mycophenolate given alone or in combination. Results We identified 204 ITP patients who had received one or more second-line therapies. The most common second-line therapies were immunosuppressant medications (n = 106; 52.0%), splenectomy (n = 106; 52.0%), TPO-RAs (n = 75; 36.8%), danazol (n = 73; 35.8%), and rituximab (n = 67; 32.8%). For patients who received only one second-line therapy (n = 88), the most common treatment was splenectomy (n = 28; 31.8%). For patients who received more than one second-line therapy (n = 116), the most common treatment sequence was splenectomy, followed by immunosuppressant medications (n = 7; 6.0%). Of the 154 evaluable patients at the end of follow-up, 69 (44.8%) achieved a complete platelet count response and 101 (65.5%) achieved a partial response. Conclusion Immunosuppressant medications and splenectomy are commonly used as second-line therapies for ITP in Canada. Treatment choices and the sequence of treatments were variable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".