Фібриноліз і кровотеча невідомої причини
Bibliographic record
Abstract
Patients with Bleeding of Unknown Cause (BUC) have a variety of bleeding symptoms ranging from mild to moderate, but no circulatory disturbances. In clinical practice, hyperfibrinolysis is not sufficiently evaluated as the main cause of bleeding, and there are no commonly known tests for abnormal fibrinolysis. Aim - to analyse the occurrence of abnormal fibrinolysis and determine the role of specific biomarkers and coagulation tests in its diagnosis, which will improve the understanding and treatment of bleeding in patients with complex coagulation disorders. Several patients have been reported with distinct fibrinolytic disorders, including α2-antiplasmin deficiency, plasminogen activator inhibitor 1 deficiency, or Quebec platelet disorder. The review aims to summarize data on these established fibrinolytic disorders and discuss the assessment of fibrinolysis in previous cohorts. In addition, we review available global tests with the potential to measure fibrinolysis, such as fibrin clot turbidity assay and rotational thromboelastometry, and their relevance to screening patients with BUC. We concluded that due to the lack of adequate global tests, hyperfibrinolysis may be an underdiagnosed cause of blood coagulation disorders. Diagnosis of hyperfibrinolytic coagulation disorders would improve patient management because effective treatment with antifibrinolytic agents is available. Conclusions. Detection of hyperfibrinolysis is important in the evaluation of patients with coagulopathy, as it can lead to serious bleeding. The use of antifibrinolytics is an effective treatment. However, complex diagnostic methods, lack of available screening tests and insufficient information about inborn defects in fibrinolytic factors can lead to underestimation of this problem. Future research and newer methods of analysis may improve the diagnosis and treatment of patients with coagulation disorders. No conflict of interests was declared by the authors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.017 |
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; both teacher heads agree on what is shown here.
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".