Bibliographic record
Abstract
Purpose of review The aim of this review is to highlight strategies being pursued to enhance current concentrate therapies for the hemophilias. During the past 5 years, significant progress has been made with a variety of protein-engineering initiatives, some of which are already in early-phase clinical trials. Recent findings The standard of care for hemophilia therapy involves the infusion of clotting factor concentrates either at the time of bleeding (on demand therapy) or in a prophylactic schedule to prevent bleeding episodes. This latter approach to therapy has been used in some parts of Europe for several decades and has recently been shown, in a prospective randomized study, to result in a significant reduction in musculoskeletal pathology. The aim of many of the novel concentrates under development is to prolong the half-life of the infused clotting factor and thus to reduce the frequency of infusions. Several different strategies are being evaluated for this purpose including conjugation with hydrophilic polymers and generation of fusion proteins that are recycled by the FcRn receptor. Summary The speed of progress with the development of several approaches to extend clotting factor half-lives has been encouraging. It is very likely that several of these concentrates will reach the clinic in the near future.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".