Disclosing incidental findings in brain research: The rights of minors in decision‐making
Bibliographic record
Abstract
MRI is used routinely in research with children to generate new knowledge about brain development. The detection of unexpected brain abnormalities (incidental findings; IFs) in these studies presents unique challenges. While key issues surrounding incidence and significance, duty of care, and burden of disclosure have been addressed substantially for adults, less empirical data and normative analyses exist for minors who participate in minimal risk research. To identify ethical concerns and fill existing gaps, we conducted a comprehensive review of papers that focused explicitly on the discovery of IFs in minors. The discourse in the 21 papers retrieved for this analysis amply covered practical issues such as informed consent and screening, difficulties in ascertaining clinical significance, the economic costs and burden of responsibility on researchers, and risks (physical or psychological). However, we found little discussion about the involvement of minors in decisions about disclosure of IFs in the brain, especially for IFs of low clinical significance. In response, we propose a framework for managing IFs that integrates practical considerations with explicit appreciation of rights along the continuum of maturity. This capacity-adjusted framework emphasizes the importance of involving competent minors and respecting their right to make decisions about disclosure.
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 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 | 0.028 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".