Notice bibliographique
Résumé
I am grateful to Ms Richards for sharing her personal experience in this area. My aim in the annotation piece was to briefly summarize existing guidelines concerning predictive genetic testing in young people for adult onset conditions and to outline some of the objections to these found in recent published work. In particular, there are three points raised by Ms Richards that I wish to address. First, Ms Richards notes that most of the published work I summarize that argues in favour of such testing has been written by people with no clinical experience in the area of predictive testing for HD. She then makes the strong contention that ‘Academic debate … may be acceptable but that the views of those with no clinical experience … should not be allowed to dictate clinical practice’. Few would agree with this claim. In fact, there are many areas of clinical practice where people who are not clinicians help to dictate practice. Health economists, lawyers, ethicists, politicians and the general public – medicine's stakeholders – all rightly contribute to clinical policy, as do the many researchers who are not clinicians. Policy is best dictated by a combination of all of the above, including clinicians. Authors putting forward arguments in favour of predictive testing in young people include Professor Peter Harper (Professor and Consultant in Medical Genetics),1 Professor Steven Roberston (Clinical Geneticist),2 Dr Julia Binedell (Clinical Psychologist),1 Dr Susan Michie (Reader in Clinical Health Psychology),3 Dr Jane Scourfield (Psychiatrist),1 Dr Jo Soldan (Clinical Psychologist),1 Neil Sharpe (member of the Genetic Testing Research Group, Canada)4 and Professor Julian Savulescu (Ethicist at the Oxford Uehiro Centre for Practical Ethics).2 Non-clinicians bring different but valuable expertise to the debate about predictive genetic testing in young people. It is also worth noting that several authors of existing guidelines (arguing against testing in young people) are not directly involved in predictive testing either; some work in Paediatrics, others in Law and others in Psychiatry.5 The opinions of non-clinicians should continue to be allowed to help dictate clinical policy, alongside the views of clinicians. Second, Ms Richards doubts that any young person under the age of 18 years would have the ‘maturity’ or ‘insight’ to undergo predictive testing and is not in favour of competency judgements replacing an age limit. However, current guidelines actually cite the possibility of providing such testing to a young person who is deemed competent. Published guidelines state that ‘substantial psychosocial benefits to the competent adolescent also may be a justification for genetic testing’.6 Guidelines even begin to outline what must occur for a young person to be deemed competent.7 Thus, my discussion of competency assessments was not a new one, I was summarizing existing guidelines. Finally, Ms Richards states that a need for psychological research is not an ethical reason for condoning the testing of minors. I agree entirely. The desire for empirical research specifically about predictive genetic testing in young people is strong.3, 5, 6, 8-11 However, it would only be ethical to research these outcomes if such testing was already occurring. Given that we now have evidence that such testing is occurring in several countries around the world, it is time for systematic empirical research to follow.12 In fact, it would be unethical not to conduct empirical research on the outcomes of such testing when the opportunity arises.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,022 | 0,032 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,051 |
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 source (Gemma direct ou Codex distillé), 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 ».