The Use of Recombinant Activated Factor VII in Patients with Glanzmann's Thrombasthenia
Bibliographic record
Abstract
Platelet transfusion is the standard treatment to control or prevent bleeding in patients with Glanzmann's thrombasthenia (GT), but platelets are often unavailable. Recombinant activated factor VII (rFVIIa) is an effective alternative to platelets in patients with GT with past/present refractoriness to platelet transfusions and antibodies to platelets. However, there is an unmet need for an alternative to platelets in patients without antibodies. This report summarizes evidence of efficacy and safety of rFVIIa in patients with GT without refractoriness or antibodies to platelets from three different sources: the Glanzmann's Thrombasthenia Registry (GTR), published literature (January 01, 1999 to December 01, 2017), and the Novo Nordisk safety surveillance database. In the GTR, 133 patients received rFVIIa for the treatment of 333 bleeding episodes and prevention of bleeding in 157 surgical procedures. Overall efficacy rates were 79 and 88%, respectively, in patients treated for bleeding episodes or for the prevention of bleeding during surgery; effectiveness was generally similar across refractoriness/antibody status categories. Median dose per infusion of rFVIIa was close to that recommended for patients with GT (90 µg/kg). Data from 14 published case reports also demonstrated that rFVIIa is effective with an acceptable safety profile in patients with GT without antibodies to platelets. Analysis of adverse events reported in GTR and in Novo Nordisk safety surveillance database did not raise any new safety concerns. These data supported the label extension of rFVIIa to include cases where platelets are not readily available, which was approved by the European Medicines Agency in December 2018.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".