Recent progress in understanding the pathogenesis of immune thrombocytopenia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Immune thrombocytopenia (ITP) is a bleeding disorder in which both antibody and cell-mediated autoimmune responses are directed against an individual's own platelets and/or megakaryocytes, leading to either enhanced platelet destruction and/or reduced platelet production, respectively. The cause of this platelet-specific autoimmunity remains unknown, but there has been a constant stream of recent publications that suggest ITP is the result of T-cell dysregulation. RECENT FINDINGS: In the last 18 months, a rich tapestry of studies has emerged that seems to clarify some immunopathologic issues in ITP while raising new questions related to ITP pathogenesis. The current view on the immunopathogenic mechanisms associated with ITP appears to particularly concentrate on how incompetent CD4+ T-regulatory cells (Tregs) allow autoimmune effector mechanisms to proceed and cause thrombocytopenia. There is a parallel body of recent literature focusing on molecular mimicry mechanisms, B-cell abnormalities, abnormal cytokine patterns and genetic studies in ITP. Of interest, one can recognize inter-relationships between these immune dysregulations. SUMMARY: This article will discuss the literature from the past 18 months pertaining to these observations and will show that whereas many of the T-cell defects have been clarified, new questions have also come to light and more immunopathological research is warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".