Legal approaches regarding health-care decisions involving minors: implications for next-generation sequencing
Bibliographic record
Abstract
The development of next-generation sequencing (NGS) technologies are revolutionizing medical practice, facilitating more accurate, sophisticated and cost-effective genetic testing. NGS is already being implemented in the clinic assisting diagnosis and management of disorders with a strong heritable component. Although considerable attention has been paid to issues regarding return of incidental or secondary findings, matters of consent are less well explored. This is particularly important for the use of NGS in minors. Recent guidelines addressing genomic testing and screening of children and adolescents have suggested that as 'young children' lack decision-making capacity, decisions about testing must be conducted by a surrogate, namely their parents. This prompts consideration of the age at which minors can provide lawful consent to health-care interventions, and consequently NGS performed for diagnostic purposes. Here, we describe the existing legal approaches regarding the rights of minors to consent to health-care interventions, including how laws in the 28 Member States of the European Union and in Canada consider competent minors, and then apply this to the context of NGS. There is considerable variation in the rights afforded to minors across countries. Many legal systems determine that minors would be allowed, or may even be required, to make decisions about interventions such as NGS. However, minors are often considered as one single homogeneous population who always require parental consent, rather than recognizing there are different categories of 'minors' and that capacity to consent or to be involved in discussions and decision-making process is a spectrum rather than a hurdle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".