Laboratory diagnosis of congenital and acquired coagulopathies, including challenging-to-diagnose rare bleeding disorders
Bibliographic record
Abstract
Coagulation factors, anticoagulant proteins, and fibrinolytic proteins are important for haemostasis and may be altered by inherited and acquired conditions. Common causes of coagulopathies include vitamin K (VK) deficiency (VKD), liver disease, lupus anticoagulants, consumption or disseminated intravascular coagulation, and much less commonly, an inherited or an acquired autoimmune coagulopathy. VKD typically accounts for ≥30% of all coagulopathy referrals, and VKD is particularly common among infants but can occur at any age and in combination with other coagulopathies. Tests for fibrinogen help assess both congenital and acquired coagulopathies, with low levels predictive of poor outcomes from diverse conditions including trauma and postpartum haemorrhage. Inherited factor deficiencies are rarer, and some affect multiple coagulation factors (F), such as combined FV and FVIII deficiency, familial deficiencies of VK-dependent clotting factors, and congenital disorders of glycosylation. Additionally, there are some rare but important disorders that uniquely impair the procoagulant/anticoagulant balance, including F5 mutations that markedly increase tissue factor pathway inhibitor in plasma, causing prolonged prothrombin and activated partial thromboplastin times, without factor deficiencies. THBD mutations that increase functional, soluble thrombomodulin in plasma can also cause bleeding. Other THBD mutations cause thrombomodulin deficiency and a consumptive coagulopathy. Bleeding disorders that result from pathogenic changes to fibrinolysis include autosomal recessive, loss-of-function mutations in SERPINE1 and SERPINF2 and an autosomal dominant gain-of-function mutation affecting PLAU, in the case of Quebec platelet disorder, which causes platelet-dependent increased fibrinolysis. Laboratories need to consider strategies for diagnosing these different conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
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".