Clinical implications of emerging pathogens in haemophilia: the variant Creutzfeldt–Jakob disease experience
Bibliographic record
Abstract
The impact of variant Creutzfeldt-Jakob disease (vCJD) on the clinical practice of haemophilia in the UK is coloured by the haemophilia community's experience of hepatitis C virus and human immunodeficiency virus (HIV) transmission via plasma-derived therapies in the 1980s, when the delay in recognizing and acting on the potential risks cost many patients their lives and left others to manage another chronic disease. This crisis prompted organisations such as the United Kingdom Haemophilia Centre Doctors' Organisation to advocate for the introduction of haemophilia therapies that would not be susceptible to contamination with blood-borne pathogens. After the identification of vCJD in 1996, a number of public health measures were taken in response to a government-sponsored vCJD risk assessment, and following reports of transfusion-transmission of vCJD, additional guidelines have been developed to prevent person-to-person transmission, some of which may impact the quality and availability of medical and surgical care. Variant CJD has had a significant negative effect on the UK haemophilia community, shaking patient confidence in the therapies they have received over the last 21 years, affecting the quality of care and creating the risk of stigmatizing the community as it was in the 1980s. As with HIV and vCJD, emerging blood-borne infectious agents will likely affect blood and blood-derived therapies well before we become aware of its presence. As a result, only therapies with the lowest level of risk should be used for care of patients with haemophilia.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".