Laboratory Diagnostics for Thrombosis and Hemostasis Testing—Part IV
Bibliographic record
Abstract
This is the fourth themed issue of Seminars in Thrombosis and Hemostasis (STH) focused on laboratory diagnostics. We are pleased to include two in-depth reviews covering the performance of D-dimer assays in clinical laboratories, as summarized by two external quality assessment (EQA) programs serving different parts of the world.[ 1 ] [ 2 ] D-dimer testing is non-standardized and non-harmonized but has important clinical uses for the exclusion of venous thromboembolism, diagnosis of disseminated intravascular coagulation, and provides prognostic information in serious illnesses, such as coronavirus disease 2019. Thus, the real-world performance of these assays has significant clinical implications. A separate comprehensive review by Undas then covers an important topic in both clinical laboratories and research settings—laboratory testing for congenital and acquired fibrinogen disorders.[ 3 ] The review covers the use of common coagulation tests but also discusses molecular diagnosis and complex research assays that study fibrin formation and properties of fibrin clots. In the subsequent review contributed by Jennings et al. on behalf of the EQATH organization, worldwide EQA results for thrombophilia assays are presented from testing performed on a plasma standard from the International Society on Thrombosis and Hemostasis Scientific and Standardization Committee.[ 4 ] The article provides proof of concept for pooling proficiency testing results from different EQA providers for analytes that are performed in only a few laboratories. Next, Jin et al. present an original research study comparing anti-Xa and activated partial thromboplastin time (aPTT) results on hospitalized patients, with a particular focus on results that are discordant regarding the degree of heparinization and whether discordant values may predict clinical outcomes.[ 5 ] Following this, Devreese provides an expert review on thrombosis in antiphospholipid syndrome, providing information on laboratory testing and how laboratory profiles can be used to risk-stratify patients and identify those at greatest risk of thrombotic events.[ 6 ] Favaloro et al. then present an informative review on laboratory testing for ADAMTS13 (a disintegrin and metalloprotease with thrombospondin type 1 motif, member 13) activity and also ADAMTS13 inhibitors.[ 7 ] In addition to discussion of use of ADAMTS13 testing to diagnose and manage thrombotic thrombocytopenic purpura (TTP), characterized by severe ADAMTS13 deficiency, the manuscript also discusses conditions with milder deficiencies and how disruption of the ADAMTS13–von Willebrand factor axis can create a prothrombotic milieu. Favaloro and Arunachalam next present a review of practice for factor VIII inhibitor testing from Australasian and Asia-Pacific EQA data.[ 8 ] The data are reassuring since participating laboratories are mostly correct in their ability to identify the presence or absence of a factor inhibitor. However, the between-laboratory coefficients of variation on individual samples (affecting inhibitor titer) are rather large, highlighting opportunities for improved technical performance and standardization. In a final review for this issue of STH, Ichinose provides detailed information about autoantibodies in autoimmune coagulation factor deficiencies.[ 9 ] For instance, these polyclonal antibodies can neutralize factor activity, accelerate factor clearance, or exhibit mixed features. The antibody characteristics have impacts on assays used to diagnose and monitor patients with these disorders. Finally for this issue, in a Letter to the Editor, we learn about three patients with dysfibrinogenemia and unusual comorbid conditions.[ 10 ] The submission also includes information about important differences between von Clauss versus prothrombin time (PT) derived fibrinogen assays in patients with dysfibrinogenemia and potential use of the PT derived fibrinogen assay as a surrogate for fibrinogen antigen testing in this setting. In summary, we are pleased to present this excellent issue of STH and hope readers value the content as much as we have during creation of the issue. Publication History Article published online: 08 August 2025 © 2025. Thieme. All rights reserved. Thieme Medical Publishers, Inc. 333 Seventh Avenue, 18th Floor, New York, NY 10001, USA
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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.
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".