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Pathophysiology and management of chronic immune thrombocytopenia: focusing on what matters

2010· review· en· W1570354759 on OpenAlexafffund
Lisa J. Toltl, Donald M. Arnold

Bibliographic record

VenueBritish Journal of Haematology · 2010
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsMcMaster UniversityCanadian Blood Services
FundersCanadian Institutes of Health Research
KeywordsImmune thrombocytopeniaMedicineThrombopoietinPathophysiologyIntensive care medicineMechanism (biology)ImmunologyDiseaseRomiplostimClinical trialEltrombopagPlateletPathogenesisImmune systemAutoimmune diseaseBioinformaticsAntibodyInternal medicineBiologyStem cell

Abstract

fetched live from OpenAlex

Immune thrombocytopenia (ITP) is a common autoimmune disease characterized by low platelet counts and an increased risk of bleeding. Antibody-mediated platelet destruction has been the prevailing hypothesis to explain ITP pathogenesis, supported by the efficacy of B-cell depletion therapy; however, the recent success of thrombopoietin receptor agonists lends support to the notion that platelet production is also insufficient. Best practice for the management of chronic ITP has not yet been established because data from comparative trials are lacking. Despite renewed interest in novel drugs capable of increasing platelet counts, ultimate treatment goals for ITP patients must be kept in mind: to improve patients' health and well-being. In this article, the pathophysiology of ITP is reviewed and key remaining questions about mechanism are explored. A rational approach to the management of ITP in adults is outlined, acknowledging evidence and evidence gaps, and highlighting the need for clinically important endpoints in future clinical trials.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.311
Teacher spread0.290 · 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

Citations54
Published2010
Admission routes2
Has abstractyes

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