Application of a hemophilia mortality framework to the Emicizumab Global Safety Database
Bibliographic record
Abstract
BACKGROUND: As the first non-factor replacement therapy for persons with congenital hemophilia A (PwcHA), emicizumab's safety profile is of particular interest to the community. OBJECTIVES: We applied an algorithm for categorization of fatal events contemporaneous to emicizumab using reporter-assessed causality documented in the Roche Emicizumab Global Safety Database. PATIENTS/METHODS: All fatalities in PwcHA reported to the database (from clinical trials, pre-market access, and spontaneous post-marketing reports) were categorized into: associated with hemophilia A-hemorrhagic, thrombotic, human immunodeficiency virus (HIV)/hepatitis C virus (HCV), hepatic (non-HCV); associated with general population-trauma/suicide, non-HA-associated conditions; or, unspecified. Reported cause of death was not reassessed. RESULTS: As of cut-off May 15, 2020, 31 fatalities in PwcHA taking emicizumab were reported. Median age at death was 58 years; 51% had factor VIII inhibitors. Fifteen fatalities were considered associated with HA; overall, the most frequent category was hemorrhage (11/31). Of these, six had a history of life-threatening bleeds, and four had a history of intracranial hemorrhage. The remaining HA-associated fatalities were related to HIV/HCV (3/31) and other hepatic causes (1/31). No cases were categorized as thrombotic. Of 10 cases considered not associated with HA, two were categorized as cardiovascular (non-thrombotic), five as infection/sepsis, and one each of trauma/suicide, pulmonary, and malignancy. Six cases were unspecified. CONCLUSIONS: No unique risk of death was associated with emicizumab prophylaxis in PwcHA. The data reveal that mortality in PwcHA receiving emicizumab was primarily associated with hemorrhage or non-HA-associated conditions, and was not reported by treaters to be related to emicizumab treatment.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".