What Are the Most Oppressing Legal and Ethical Issues Facing Biorepositories and What Are Some Strategies to Address Them?
Notice bibliographique
Résumé
everal major legal and ethical requirements that may greatly hamper the operations of biorepositories have only been proposed; however, in the future such requirements could negatively impact biorepositories as well as biomedical research in general.Two examples are the following:1) The return of research results to patients: While this issue affects biorepositories worldwide, some of the legal issues that complicate this topic are national.One legal issue in the United States is that most research laboratories are not certified via the Clinical Laboratory Improvement Amendments (CLIA) and laboratory data provided to patients or their physicians must be performed by a CLIA certified laboratory.Thus, it is illegal to provide patients with most biomedical information generated in research.Most important, as the name implies, ''research data'' are not validated clinically.These data may be wrong (e.g., the methods used to collect the data may be invalid, mistakes may occur in analysis or interpretation, the data may be fraudulent or misinterpreted, bias may be responsible for the conclusions, and/or the data may only apply to one subpopulation).Of note, if such incorrect information is used in making medical decisions, harm may be caused to the individuals to whom the research information is provided.With whom does liability reside?This would be an unfunded mandate; who would be responsible and pay for the huge amount of work associated with the transfer of research data, for the development of the informatics systems needed for this activity and for costs of repeating and verifying results?Because human tissues as well as clinical information are supplied de-identified to investigators, the cost of these unfunded mandates would likely fall on biorepositories.Because biorepositories typically have no clinical relationship with the source of specimens, cold contacts with patients to provide research data would be very problematic, legally and ethically.Most institutions would not accept such potential liability, other risks, and costs associated with such requirements, so the number of human biorepositories would be reduced, which would result in a great reduction of research.I would not agree to return research results to patients.2) Informed consent for the use of all human tissues in research:
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».