Rare bleeding disorders: Advances in management
Bibliographic record
Abstract
Inherited factor coagulation deficiencies and vascular bleeding disorders, associated with bleeding of various severity, are often classified as rare bleeding disorders (RBDs). These include inherited fibrinogen disorders, inherited platelet function disorders (IPFD) and hereditary haemorrhagic telangiectasia (HHT). In the last decades, there have been large increases in knowledge on the epidemiology, genetics, physiopathology, clinical features, and diagnosis of RBDs, but improvements in management have been more limited and remain challenging. The treatment mainstay of RBDs is based only on replacement of a few available coagulation factor concentrates or cryoprecipitates. There is growing interest in therapeutic agents that enhance coagulation or inhibiting anticoagulant pathways in RBDs. In severe IPFD, the optimal platelet transfusion strategy is not yet established. Moreover, data is scarce on the effectiveness and safety of desmopressin and/or antifibrinolytic drugs often used for milder IPFD treatment. The best fibrinogen replacement strategy (prophylaxis vs. on demand) in afibrinogenemia is still debated. Similarly, the optimal trough fibrinogen target level for treatment of acute bleeding, and the role of fibrinogen replacement during pregnancy in mild hypofibrinogenemia and dysfibrinogenemia, have not been properly evaluated. The therapeutic arsenal in HHT includes antifibrinolytics and a series of antiangiogenic agents whose potential efficacy has been tested in small studies or are under investigation for treatment of bleeding. However, there is need to address several issues, including the optimal dosing strategies, the potential emergent toxicity of longer-term use, and the impact of systemic antiangiogenic treatment on visceral arteriovenous malformations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".