Consensus opinion for the selection and use of therapeutic products for the treatment of haemophilia in Spain
Bibliographic record
Abstract
The period between isolation of HIV in the early 1980s and the development of effective viral inactivation procedures able to eradicate the virus from the blood supply was long and unfortunately many recipients of blood-derived products became infected; this translated into a devastating impact on their quality of life, quality of care as well as on their life expectancy. Some years later, hepatitis C virus infection was identified as another known blood-borne disease complicating the treatment of haemophilia. Nowadays, the potential threat of emerging new pathogens has stressed the need to provide effective but primarily safe products with regard to infectious agents, as well as to regularly update therapeutic guidelines for haemophilia. The aim of the present publication was to review some of the crucial aspects related to the choice of haemostatic concentrates for the treatment of haemophilia and other inherited bleeding disorders, to analyse the current situation in the United States, Canada and European Union countries and to report the most relevant aspects of the Spanish consensus opinion of haemophilia-treating doctors for the use of therapeutic products for haemophilia recently issued. Essentially, it suggests that a gradual switch to recombinant concentrates may be a beneficial decision for patients with haemophilia and for the National Health Service.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".