Severe hemorrhage in children with newly diagnosed immune thrombocytopenic purpura
Bibliographic record
Abstract
Controversy exists regarding management of children newly diagnosed with immune thrombocytopenic purpura (ITP). Drug treatment is usually administered to prevent severe hemorrhage, although the definition and frequency of severe bleeding are poorly characterized. Accordingly, the Intercontinental Childhood ITP Study Group (ICIS) conducted a prospective registry defining severe hemorrhage at diagnosis and during the following 28 days in children with ITP. Of 1106 ITP patients enrolled, 863 were eligible and evaluable for bleeding severity assessment at diagnosis and during the subsequent 4 weeks. Twenty-five children (2.9%) had severe bleeding at diagnosis. Among 505 patients with a platelet count less than or equal to 20 000/mm(3) and no or mild bleeding at diagnosis, 3 (0.6%), had new severe hemorrhagic events during the ensuing 28 days. Subsequent development of severe hemorrhage was unrelated to initial management (P = .82). These results show that severe bleeding is uncommon at diagnosis in children with ITP and rare during the next 4 weeks irrespective of treatment given. We conclude that it would be difficult to design an adequately powered therapeutic trial aimed at demonstrating prevention of severe bleeding during the first 4 weeks after diagnosis. This finding suggests that future studies of ITP management should emphasize other outcomes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".