Assessing Decision-Making Capacity in the Behaviorally Nonresponsive Patient With Residual Covert Awareness
Bibliographic record
Abstract
Recent neuroscientific findings suggest that functional magnetic resonance imaging (fMRI)-based brain–computer interfaces may be a viable strategy for detecting covert awareness in patients clinically diagnosed as being in a vegetative state. This research may open a promising new avenue for developing neuroimaging techniques that provide prognostic and diagnostic information that complements current behavioral tests for assessing disorders of consciousness, thereby increasing the effectiveness of diagnostic screening. These techniques may also permit patients who are behaviorally nonresponsive yet retain high levels of preserved cognition to meaningfully engage in clinical decision making. Before this application can occur, certain ethical issues associated with decision-making capacity must be addressed. Although it is not currently possible to assess decision-making capacity through neuroimaging methods, it may be in the future, provided that certain conceptual and empirical steps are taken to demonstrate that brain–computer interfaces satisfy requisite criteria of capacity assessment. In this article we lay out the conceptual foundations for a mechanistic explanation of capacity that would allow the necessary empirical steps for incorporating neuroimaging techniques into the standard capacity assessment battery utilized in clinical practice.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".