How much prophylaxis is enough in haemophilia?
Bibliographic record
Abstract
INTRODUCTION: Prophylaxis has become standard of care for all persons with haemophilia (PWH) with a severe phenotype. However, 'standard prophylaxis' with either factor or non-factor therapies (currently only emicizumab available) is prohibitively expensive for much of the world. We sought to address the question of 'How much prophylaxis is enough?' and 'Can it be individualized?' and specifically 'Can emicizumab be individualized?'. METHODS: We reviewed the literature on prophylaxis in haemophilia since its inception in the 1950s to the present, the development of more and less intense factor prophylaxis regimens and their outcomes and additionally the published outcomes of prophylaxis with low dose emicizumab. RESULTS: What these experiences collectively show is that low dose emicizumab does result in significant benefits to patients whilst being much less expensive than a "one size fits all" emicizumab prophylaxis approach. We also took note that some non-factor therapies still in development are individualized given that high doses of these can potentially put patients at risk. CONCLUSIONS: Prophylaxis is now clearly accepted as standard of care for PWH with a severe phenotype but now in a very short time a large assortment of different treatment options for prophylaxis have become/are becoming available and the haemophilia community will need to determine how to best use these recognizing that no 'one treatment fits all'.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".