Paediatric MRI Research Ethics: The Priority Issues
Bibliographic record
Abstract
In this paper we first briefly describe neuroimaging technology our reasons for studying magnetic resonance imaging MRI technology and then provide a discussion of what we have identified as priority issues for paediatric MRI research We examine the issues of respectful involvement of children in the consent process as well as privacy and confidentiality for this group of MRI research participants In addition we explore the implications of unexpected findings for paediatric MRI research participants Finally we explore the ethical issues concerning advances in functional MRI This paper aims to provide a clear description of priority paediatric MRI research ethics issues to make some preliminary recommendations regarding next steps
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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 | 0.285 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.025 | 0.043 |
| Insufficient payload (model declined to judge) | 0.003 | 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".