Accelerated Fibrinolysis: A Tendency to Bleed?
Bibliographic record
Abstract
Hyperfibrinolysis is rarely investigated as an underlying mechanism in patients with mild-to-moderate bleeding disorders (MBDs) and bleeding disorders of unknown cause (BDUC). Hereditary hyperfibrinolytic disorders, including α2-antiplasmin (α2-AP) deficiency, plasminogen activator inhibitor type 1 (PAI-1) deficiency, Quebec platelet disorder, and tissue plasminogen activator (tPA) excess, present with mild-to-moderate bleeding symptoms that are common in patients with MBD or BDUC, but may also manifest as life-threatening bleeding. This review summarizes the available data on hyperfibrinolysis in MBD and BDUC patients, and its assessment by various methods such as measurement of fibrinolytic factors, global hemostatic assays (e.g., viscoelastic testing, turbidity-based plasma clot lysis), and fluorogenic plasmin generation (PG). However, evidence on the relationship between hyperfibrinolytic profiles and bleeding severity is inconsistent, and, although found in some coagulation factor deficiencies, has not been universally observed. In BDUC, increased tPA activity and paradoxical increases in thrombin-activatable fibrinolysis inhibitor and α2-AP have been reported. Some studies reported no change in PAI-1 levels, while others observed reduced PAI-1 levels in a significant subset of patients. The tPA-ROTEM (tPA-rotational thromboelastometry) assay identified a hyperfibrinolytic profile in up to 20% of BDUC patients. PG analysis revealed a paradoxically reduced peak plasmin, but showed strong predictive power in differentiating BDUC patients from healthy controls. Although global fibrinolytic assays may help identify hyperfibrinolytic profiles as a potential cause of increased bleeding in some MBD or BDUC patients, the utility of measuring fibrinolytic factors requires further investigation. Tranexamic acid is commonly used to treat hereditary hyperfibrinolysis and is also recommended in MBD/BDUC patients prior to hemostatic challenges.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".