Ethical issues in haemophilia
Bibliographic record
Abstract
Ethical issues surrounding both the lack of global access to care as well as the implementation of advancing technologies, continue to challenge the international haemophilia community. Haemophilia is not given the priority it deserves in most developing countries. Given the heavy burdens of sickness and disease and severe resource constraints, it may not be possible to provide effective treatment to all who suffer from the various 'orphan' diseases. Nevertheless, through joint efforts, some package of effective interventions can be deployed for a significant number of those who are afflicted with 'orphan' diseases. With cost-effective utilization of limited resources, a national standard of care is possible and affordable. Gene-based diagnosis carries attendant ethical concerns whether for clinical testing or for research purposes, even as the list of its potential benefits to the haemophilia community grows rapidly. As large-scale genetic sequencing becomes quicker and cheaper, moving from the research to the clinic, we will face decisions about the implementation of prenatal, neonatal and other screening programs. Such debates will require input from not just the health care professionals but from all stakeholders in the haemophilia community. Finally, long-term therapeutic success gene transfer in small and large animal models raises the question of when and in which patient population the novel therapeutic approach should first be studied in humans with haemophilia. Although gene therapy represents a worthy goal, the central question for the haemophilia community should be whether it wishes to volunteer itself as a model for a much broader set of innovations.
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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 | 0.103 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.033 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".