Switching patients in the age of long-acting recombinant products?
Bibliographic record
Abstract
Introduction: Prophylaxis with factor replacement therapy is the gold standard for the treatment of hemophilia, but this often requires frequent infusions. A number of long-acting factor products have been developed to reduce the burden on patients.Areas covered: This is an overview of information presented at two symposia held at the World Federation of Hemophilia and International Society on Thrombosis and Haemostasis – Scientific and Standardization Committee annual meetings. The pharmacokinetic, safety and efficacy data for long-acting recombinant products are reviewed, with a focus on recombinant factor IX albumin fusion protein (rIX-FP) and rVIII-SingleChain. This overview also provides a guide for managing a patient’s switch to long-acting products.Expert opinion: Long-acting products may allow patients to maintain or decrease bleeding rates whilst increasing their dosing interval, which may in turn reduce the burden on patients and caregivers. When switching patients to long-acting products health-care professionals should provide balanced and thorough education to the patient, whilst supporting their emotional well-being. Regimens should address patients’ needs and goals but should also be guided by clinical phenotype and pharmacokinetic assessment. Follow-up should assess safety concerns, bleeding rates, joint health and the impact of the regimen on patients’ lifestyle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".