The current state of adverse event reporting in hemophilia
Bibliographic record
Abstract
INTRODUCTION: Replacement of the missing clotting factor is the mainstay of hemophilia treatment. Whilst historically many hemophilia patients were infected with blood-borne viruses transmitted via plasma-derived products, nowadays the formation of alloantibodies against the missing clotting factor is the main adverse event of treatment. Areas covered: This paper provides an overview of the current national and international adverse event reporting systems, what these surveillance schemes taught us about side effects of the products presently in use, and elaborates on how to adapt these systems to the challenges we face with the changing treatment landscape. Expert commentary: Treatment of inherited bleeding disorders was accompanied by severe complications in the past, resulting in major morbidity and mortality. Current products are much safer, but still require monitoring via efficient safety surveillance systems. Adverse events are reported in national and international systems. With many new products entering the market, as well as non-factor replacement therapies, new safety issues may arise. It is important to identify potential adverse events early by making surveillance systems suitable to pick up unknown or unexpected effects, and to recognize and communicate patterns of adverse events rapidly.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".