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Record W4414871780 · doi:10.1016/j.rpth.2025.103210

Haemostasis alterations in immune thrombocytopenia and their clinical significance

2025· editorial· en· W4414871780 on OpenAlexaff
Thomas Pincez, Natalie Mathews, Arnaud Bonnefoy

Bibliographic record

VenueResearch and Practice in Thrombosis and Haemostasis · 2025
Typeeditorial
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPlateletImmune thrombocytopeniaClinical significanceImmune systemPlatelet activationBasal (medicine)HemostasisPlatelet disorderSevere bleeding

Abstract

fetched live from OpenAlex

Immune thrombocytopenia (ITP) is highly heterogeneous, and only a subset of patients with severe thrombocytopenia develops significant bleeding. ITP is also associated with a paradoxical increased risk of thrombosis. Here, we review the multiple haemostasis alterations reported in patients with ITP. Data show an increased platelet basal activation with lower platelet reactivity but with overall increased platelet function. The activated state of platelets and the endothelial dysfunction lead to an activation of coagulation. These alterations tend to counterbalance the bleeding tendency related to thrombocytopenia but have some variability among patients. Consequently, several functional assays have correlated the magnitude of haemostasis alterations to bleeding risk. These results suggest that ITP is a multifaceted bleeding disorder that goes beyond simple thrombocytopenia. The heterogeneity in the various alterations in haemostatic function is likely to drive the variable bleeding phenotypes observed. Haemostasis functional assays could thus serve as clinically relevant tools to individualize management.

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)0.003
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0050.004

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 head0.165
GPT teacher head0.497
Teacher spread0.332 · 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
GenreEditorial

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

Citations2
Published2025
Admission routes1
Has abstractyes

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