Rare inherited coagulation and fibrinolytic defects that challenge diagnostic laboratories
Bibliographic record
Abstract
BACKGROUND: Coagulation factors, anticoagulants, and fibrinolytic proteins are important for hemostasis, and mutations affecting these proteins causes some rare inherited bleeding disorders that are particularly challenging to diagnose. AIMS: This review provides current information on rare inherited bleeding disorders that are difficult to diagnose. MATERIAL & METHODS: A review of the literature was conducted for up to date information on rare and difficult to diagnose bleeding disorders. RESULTS: Some rare bleeding disorders cause an inherited deficiency of multiple coagulation factors (F), such as combined FV and FVIII deficiency and familial vitamin K-dependent clotting factor deficiency. Additionally, congenital disorders of glycosylation can affect a variety of procoagulant and anticoagulant proteins and also platelets. Some bleeding disorders reflect mutations with unique impairments in the procoagulant/anticoagulant balance, including those caused by F5 mutations that secondarily increase the plasma levels of tissue factor pathway inhibitor as well as THBD mutations that increase functional thrombomodulin in plasma or cause a consumptive coagulopathy due to thrombomodulin deficiency. Some bleeding disorders accelerate fibrinolysis due to loss-of-function mutations in SERPINE1 and SERPINF2 or in the case of Quebec platelet disorder, a duplication mutation that rewires PLAU and selectively increases expression in megakaryocytes, resulting in a unique platelet-dependent gain-of-function defect in fibrinolysis. DISCUSSION: Current information on rare and difficult to diagnose bleeding disorders indicates they have unique clinical and laboratory features, and pathogenic characteristics to consider for diagnostic evaluation. CONCLUSION: Laboratories and clinicians should consider rare inherited disorders, and difficult to diagnose conditions, in their strategy for diagnosing bleeding disorders.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.003 |
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".