Ethical issues related to clinical research and rare diseases
Bibliographic record
Abstract
The Alpha-1 Foundation (Foundation) has a long history of recognizing the importance of ethical, legal, and social issues (ELSI) in rare genetic disease research [1].In April of last year, the Foundation assembled a distinguished panel of ethicists, social scientists, clinical investigators, and legal experts to explore ELSI issues related to research in rare diseases.Marilyn Coors, PhD, Chair of the ELSI Working Group of the Foundation and of the meeting, explained the conference goal was to "discuss the ethics of rare disease research and, if possible, make recommendations that can inform future clinical research in rare diseases".She continued "if we can't accomplish those two charges with the people who are here today, I don't know who can".Clinical research in rare diseases such as alpha-1 antitrypsin deficiency (AATD) encounters unique challenges including a small patient population for recruitment, limited research funding, and an urgent need for rapid drug development.Efforts have been under way to streamline research in rare diseases through flexible study protocols, while protecting the rights and interests of volunteer participants.The goal of this conference was to foster discussion about critical and emerging ethical issues in rare disease research that could contribute to the formulation of recommendations about future research.
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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 | 0.232 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.028 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".