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Record W2040021588 · doi:10.1097/moh.0b013e3283567541

The immunopathogenesis of immune thrombocytopenia

2012· review· en· W2040021588 on OpenAlexaff
John W. Semple, Drew Provan

Bibliographic record

VenueCurrent Opinion in Hematology · 2012
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsSt. Michael's HospitalCanadian Blood Services
Fundersnot available
KeywordsImmunologyPathophysiologyImmune systemMedicineAutoimmunityPlateletMegakaryocyteBiologyInternal medicineStem cell

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Immune thrombocytopenia (ITP) is an autoimmune bleeding disorder in which T and B cells recognize platelet antigens and initiate antiplatelet destructive mechanisms such as peripheral Fc receptor-mediated phagocytosis in the spleen or megakaryocyte destruction/inhibition within the bone marrow. The purpose of this review is to report on the ITP pathophysiology literature published from January 2011 to early in 2012. RECENT FINDINGS: The underlying stimulus of platelet autoimmunity is not known; however, in 2011, as in previous years, there has been a significant contribution of published studies addressing the pathophysiology of ITP. At least half of the 2011 ITP pathophysiology literature was associated with T-cell dysregulation particularly with respect to T-helper 17 cell and related cytokine and genetic studies. There were also studies related to B-cell responses, human spleen cells and the potential role of oxidative stress in ITP. With respect to therapeutic research, the mechanisms of action of intravenous gammaglobulin relating to Fc inhibitory receptors and sialylation have been challenged. SUMMARY: The overall landscape of pathophysiological research into ITP still is overwhelmed by studies on abnormal T-cell responses and these studies are beginning to clarify the underlying immune mechanisms that are responsible for the disorder.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.136
GPT teacher head0.419
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations80
Published2012
Admission routes1
Has abstractyes

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