New Insights Into the Treatment of Glanzmann Thrombasthenia
Bibliographic record
Abstract
Glanzmann thrombasthenia (GT) is a rare inherited autosomal recessive bleeding disorder of platelet function caused by a quantitative or qualitative defect of platelet membrane glycoprotein IIb/IIIa (integrin αIIbβ3), a fibrinogen receptor required for platelet aggregation. Bleeds in GT are variable and may be severe and unpredictable. Bleeding not responsive to local and adjunctive measures, as well as surgical procedures, is treated with platelets, recombinant activated factor VII (rFVIIa), or antifibrinolytics, alone or in combination. Although platelets are the standard treatment for GT, their use is associated with the risk of blood-borne infection transmission and may also cause the development of platelet antibodies (to human leukocyte antigens and/or αIIbβ3), potentially resulting in platelet refractoriness. Currently, where rFVIIa is approved for use in GT, this is mostly for patients with platelet antibodies and/or a history of platelet refractoriness. However, data from the prospective Glanzmann's Thrombasthenia Registry (829 bleeds and 206 procedures in 218 GT patients) show that rFVIIa was frequently used in nonsurgical and surgical bleeds, with high efficacy rates, irrespective of platelet antibodies/refractoriness status. The mechanisms underpinning rFVIIa effectiveness in GT have been studied. At therapeutic concentrations, rFVIIa binds to activated platelets and directly activates FX to FXa, resulting in a burst of thrombin generation. Thrombin converts fibrinogen to fibrin and also enhances GT platelet adhesion and aggregation mediated by the newly converted (polymeric) fibrin, leading to primary hemostasis at the wound site. In addition, thrombin improves the final clot structure and activates thrombin-activatable fibrinolysis inhibitor to decrease clot lysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".