Establishment of a framework for assessing mortality in persons with congenital hemophilia A and its application to an adverse event reporting database
Bibliographic record
Abstract
BACKGROUND: Despite recent therapeutic advances, life expectancy in persons with congenital hemophilia A (PwcHA) remains below that of the non-HA population. As new therapies are introduced, a uniform approach to the assessment of mortality is required for comprehensive evaluation of risk-benefit profiles, timely identification of emerging safety signals, and comparisons between treatments. OBJECTIVES: Develop and test a framework for consistent reporting and analysis of mortality across past, current, and future therapies. PATIENTS/METHODS: We identified known causes of mortality in PwcHA through literature review, analysis of the US Food and Drug Administration Adverse Event Reporting System (FAERS) database, and expert insights. Leading causes of death in general populations are those recognized by the Centers for Disease Control and Prevention and the World Health Organization. We developed an algorithm for assessing fatalities in PwcHA and used this to categorize FAERS data as a proof of concept. RESULTS: PwcHA share mortality causes with the non-HA population including cardiovascular disease, malignancy, infections, pulmonary disease, dementias, and trauma/suicide. Causes associated with HA include hemorrhage, thrombosis, human immunodeficiency virus, hepatitis C virus, and liver dysfunction. We propose an algorithm employing these classes to categorize fatalities and use it to classify FAERS fatality data between 01/01/2000 and 03/31/2020; the most common causes were hemorrhage (22.2%) and thrombosis (10.4%). CONCLUSIONS: A conceptual framework for examining mortality in PwcHA receiving any hemophilia therapy is proposed to analyze and interpret fatalities, enabling consistent and objective assessment. Application of the framework using FAERS data suggests a generally consistent pattern of reported mortality across HA treatments, supporting the utility of this unified approach.
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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 | 0.069 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".