ArticleHemaSphere2023
Prediction of the Clinical Course of Immune Thrombocytopenia in Children by Platelet Kinetics.
Article in HemaSphere, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 0 citations in OpenAlex.
- The prediction of disease chronicity when immune thrombocytopenia is first diagnosed.Translational pediatrics · 2026Article
- Artificial intelligence prediction of therapy response in newly diagnosed adult severe primary immune thrombocytopenia.Research and practice in thrombosis and haemostasis · 2026Article
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic-pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The K
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Registered trials
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