Neuroimaging for Disorders of Consciousness: Ethical Priorities in Research, Policy, and Translation
Bibliographic record
Abstract
Background: Acquired brain injury is a critical health problem in Canada, placing greater demands on health resources as improvements in intensive care lead to more patients in long-term care. Clinical diagnosis of patients with disorders of consciousness remains difficult, but advances in neuroimaging research have the potential to reshape clinical management of such patients or provide unprecedented ways to communicate with them. Building on our earlier work, this study identifies ethically salient priorities for research and policy before translation of this promising technology. Methods: We interviewed 27 Canadian researchers, ethicists, lawyers, practitioners, allied health care professionals, and patient advocacy leaders, with expertise in neuroimaging or disorders of consciousness. Interviews were semi-structured and data were analyzed for emergent themes. Results: Participants were optimistic that neuroimaging could lead to improved clinical care. They discussed mitigating the risks of misinterpreting results and communication, creating guidelines for clinical use, and defining legal competence in this neuroimaging context as key ethical priorities for translation. Conclusions: The transition of neuroimaging techniques for disorders of consciousness from research to clinical care may yield substantial benefits to these patients, but first requires resolution of research, policy, and translational issues.
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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 | 0.231 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.087 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".