Fibrinolysis and bleeding of unknown cause
Bibliographic record
Abstract
Patients with bleeding of unknown cause (BUC) present with a variety of mild to moderate bleeding symptoms, but no hemostatic abnormalities can be found. Hyperfibrinolysis is rarely evaluated as the underlying cause for bleeding in clinical practice, and well-established global assays for abnormal fibrinolysis are lacking. Few patients with definitive fibrinolytic disorders, including α2-antiplasmin deficiency, plasminogen activator inhibitor 1 deficiency, or Quebec platelet disorder, have been reported. This review aims to summarize data on established fibrinolytic disorders and to discuss assessments of fibrinolysis in prior bleeding cohorts. Furthermore, we review available global tests with the potential to measure fibrinolysis, such as turbidity fibrin clot assays and rotational thromboelastometry, and their relevance in the workup of patients with BUC. We conclude that, due to the lack of adequate global tests, hyperfibrinolysis might be an underdiagnosed cause for a bleeding disorder. The diagnosis of hyperfibrinolytic bleeding disorders would improve patient care as effective treatment with antifibrinolytic agents is available.
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 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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".