Therapeutic innovations in hemophilia: the essential role of a positive reinvestment cycle
Bibliographic record
Abstract
ABSTRACT: Hemophilia stands out among rare genetic diseases for its significant therapeutic advancements, closely tied to substantial financial investments. Key factors driving this progress include severe hemorrhagic consequences from an early age, its impact on royal families, the HIV and hepatitis C contamination tragedies, the identification of factor VIII (FVIII) and FIX genes, and advancements in biotechnology. Maintaining low, measurable concentrations of FVIII or FIX in the blood has proven pivotal in improving patient outcomes. The mobilization of the global hemophilia community, led by the World Federation of Hemophilia, the European Association for Haemophilia and Allied Disorders, and the National Bleeding Disorder Foundation, has continuously advocated for access to safe, effective treatments. With reinvestments from biopharmaceutical partners, revolutionary options, including gene therapy, have emerged. However, this cycle of innovation and investment, essential for curing all patients worldwide, faces potential threats. This article aims to highlight the critical importance of investing in hemophilia treatment and research, a topic of concern for all stakeholders within the hemophilia community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".