Conducting Research with Highly Portable MRI in Community Settings: A Practical Guide to Navigating Ethical Issues and ELSI Checklist
Bibliographic record
Abstract
Highly portable and accessible MRI technology will allow researchers to conduct field-based MRI research in community settings. Previous guidance for researchers working with fixed MRI does not address the novel ethical, legal, and societal issues (ELSI) of portable MRI (pMRI). Our interdisciplinary Working Group (WG) previously identified 15 core ELSI challenges associated with pMRI research and recommended solutions. In this article, we distill those detailed recommendations into a Portable MRI Research ELSI Checklist that offers practical operational guidance for researchers contemplating using this technology.
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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 | 0.098 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.028 |
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".