The International Prospective Glanzmann Thrombasthenia Registry: Pediatric Treatment and Outcomes
Bibliographic record
Abstract
Abstract Background Standard treatment for Glanzmann thrombasthenia (GT), a severe inherited bleeding disorder, is platelet transfusion. Recombinant activated factor VII (rFVIIa) is reported to be effective in GT with platelet antibodies and/or refractoriness to platelet transfusions. Methods We evaluated rFVIIa effectiveness and safety for the treatment and prevention of surgical and nonsurgical bleeding in children <18 years old, with or without platelet antibodies and/or refractoriness, as reported in the GT Registry (GTR). Data were used from the GTR, an international, multicenter, observational, postmarketing study of rFVIIa that prospectively collected data on the treatment and outcomes of bleeds in patients with GT. Only patients with a diagnosis of congenital GT were included in the registry. Results Between 2007 and 2011, 27 children were treated for 44 surgical procedures (minor: 36; major: 8); nonsurgical bleeds occurred in 104 patients (599 episodes: severe, 145; moderate, 454; spontaneous, 423; posttraumatic, 176). The effectiveness of treatment for minor procedures, major procedures, nonsurgical bleeds was 6/6, 1/1, and 75/84 for rFVIIa, 6/6, 2/2, and 64/76 for rFVIIa + antifibrinolytics (AF), 11/12, 1/1, and 162/214 for platelets ± AF, and 5/6, 0/3, and 33/45 for rFVIIa + platelets ± AF. In all, 25 adverse events were reported in children; no thromboembolic events were reported. Conclusion For all patients, regardless of platelet antibody or refractoriness status, rFVIIa, administered with or without platelets (± AF), provided effective hemostasis with a low frequency of adverse events in surgical, as well as nonsurgical, bleeding in patients with GT. clinicaltrials.gov identifier: NCT01476423.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".