Independent adjudicator assessments of platelet refractoriness and rFVIIa efficacy in bleeding episodes and surgeries from the multinational Glanzmann's thrombasthenia registry
Bibliographic record
Abstract
Glanzmann's thrombasthenia (GT) is a rare congenital bleeding disorder associated with decreased platelet aggregation due to qualitative/quantitative deficiencies of the fibrinogen receptor. Severe bleeding episodes and perioperative bleeding are typically managed with platelet transfusions, although patients can develop anti-platelet antibodies or experience clinical refractoriness. The GT Registry (GTR) was established to collect efficacy/safety data on hemostatic treatments for GT, including recombinant factor VIIa (rFVIIa). At the request of the United States Food and Drug Administration, three hematology experts evaluated platelet refractoriness, antibody status, and rFVIIa efficacy data on a case-by-case basis to support a potential indication for rFVIIa in GT. Adjudication included 195 patients with 810 events (619 severe bleeding episodes, 192 surgeries), and a consensus algorithm was developed to describe adjudicators' coding of refractoriness and antibody status based on treatment patterns over time. Most rFVIIa-treated events were in patients without refractoriness or antibodies. Adjudicators rated most rFVIIa-treated bleeding episodes as successful (251/266, 94.4%; rFVIIa only, 101/109, 92.7%; rFVIIa ± platelets ± other agents, 150/157, 95.5%); efficacy was consistent in patients with platelet refractoriness ± antibodies (75/79, 94.9%), antibodies only (10/10, 100.0%), and neither/unknown (166/177, 93.8%). Adjudicators also rated most rFVIIa-treated surgeries as successful (159/160, 99.4%; rFVIIa only, 65/66, 98.5%; rFVIIa ± platelets ± other agents, 94/94, 100.0%); efficacy was consistent in patients with platelet refractoriness ± antibodies (69/70, 98.6%), antibodies only (24/24, 100.0%), and neither/unknown (66/66, 100.0%). Unblinding the adjudicators to investigator efficacy ratings changed few assessments. Doses of rFVIIa were narrowly distributed, regardless of other hemostatic agents used.
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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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".