Second‐line treatments in children with immune thrombocytopenia: Effect on platelet count and patient‐centered outcomes
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is an autoimmune bleeding disorder with isolated thrombocytopenia and hemorrhagic risk. While many children with ITP can be safely observed, treatments are often needed for various reasons, including to decrease bleeding, or to improve health related quality of life (HRQoL). There are a number of available second-line treatments, including rituximab, thrombopoietin-receptor agonists, oral immunosuppressive agents, and splenectomy, but data comparing treatment outcomes are lacking. ICON1 is a prospective, multi-center, observational study of 120 children starting second-line treatments for ITP designed to compare treatment outcomes including platelet count, bleeding, and HRQoL utilizing the Kids ITP Tool (KIT). While all treatments resulted in increased platelet counts, romiplostim had the most pronounced effect at 6 months (P = .04). Only patients on romiplostim and rituximab had a significant reduction in both skin-related (84% to 48%, P = .01 and 81% to 43%, P = .004) and non-skin-related bleeding symptoms (58% to 14%, P = .0001 and 54% to 17%, P = .0006) after 1 month of treatment. HRQoL significantly improved on all treatments. However, only patients treated with eltrombopag had a median improvement in KIT scores at 1 month that met the minimal important difference (MID). Bleeding, platelet count, and HRQoL improved in each treatment group, but the extent and timing of the effect varied among treatments. These results are hypothesis generating and help to improve our understanding of the effect of each treatment on specific patient outcomes. Combined with future randomized trials, these findings will help clinicians select the optimal second-line treatment for an individual child with ITP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".