To disclose, or not to disclose? Context matters
Bibliographic record
Abstract
Progress in understanding childhood disease using next-generation sequencing (NGS) portends vast improvements in the nature and quality of patient care. However, ethical questions surrounding the disclosure of incidental findings (IFs) persist, as NGS and other novel genomic technologies become the preferred tool for clinical genetic testing. Thus, the need for comprehensive management plans and multidisciplinary discussion on the return of IFs in pediatric research has never been more immediate. The aim of this study is to explore the views of investigators concerning the return of IFs in the pediatric oncology research context. Our findings reveal at least four contextual themes underlying the ethics of when, and how, IFs could be disclosed to participants and their families: clinical significance of the result, respect for individual, scope of professional responsibilities, and implications for the healthcare/research system. Moreover, the study proposes two action items toward anticipatory governance of IF in genetic research with children. The need to recognize the multiplicity of contextual factors in determining IF disclosure practices, particularly as NGS increasingly becomes a centerpiece in genetic research broadly, is heightened when children are involved. Sober thought should be given to the possibility of discovering IF, and to proactive discussions about disclosure considering the realities of young participants, their families, and the investigators who recruit them.
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 | 0.026 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".