Canadian Perspectives on the Clinical Actionability of Neuroimaging in Disorders of Consciousness
Bibliographic record
Abstract
BACKGROUND: Acquired brain injury is a critical public health and socioeconomic problem in Canada, leaving many patients in vegetative, minimally conscious, or locked-in states, unresponsive and unable to communicate. Recent advances in neuroimaging research have demonstrated residual consciousness in a few exemplary patients with acquired brain injury, suggesting potential misdiagnosis and changes in prognosis. Such progress, in parallel with research using multimodal brain imaging technologies in recent years, has promising implications for clinical translation, notwithstanding the many challenges that impact health care and policy development. This study explored the perspectives of Canadian professionals with expertise either in neuroimaging research, disorders of consciousness, or both, on the potential clinical applications and implications of imaging technology. METHODS: Twenty-two professionals from designated communities of neuroimaging researchers, ethicists, lawyers, and practitioners participated in semistructured interviews. Data were analyzed for emergent themes. RESULTS: The five most dominant themes were: (1) validation and calibration of the methods; (2) informed consent; (3) burdens on the health care system; (4) implications for the Canadian health care system; and (5) possibilities for improved prognosis. CONCLUSIONS: Movement of neuroimaging from research into clinical care for acquired brain injury will require careful consideration of legal and ethical issues alongside research reliability, responsible distribution of health care resources, and the interaction of technological capabilities with patient outcome.
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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 | 0.050 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.037 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".