Individualized assessment of residual cognition in patients with disorders of consciousness
Bibliographic record
Abstract
Patients diagnosed with disorders of consciousness show minimal or inconsistent behavioural evidence of conscious awareness. However, using functional neuroimaging, recent research in clinical neuroscience has identified a subpopulation of these patients who reliably produce neural markers indicative of awareness. In this study, we recorded electroencephalograms during a response-free movie task to assess narrative processing in patients with disorders of consciousness. Thirteen patients diagnosed with a disorder of consciousness and 28 healthy controls participated in this study. We designed a movie-watching/listening paradigm involving two suspenseful movie clips, one audiovisual and one audio-only, and used electroencephalography to extract patterns of brain activity that were maximally correlated between subjects. These activity patterns served as electrophysiological indices of narrative processing, which were compared to the neural responses of patients during the same movies. Our analysis revealed two patterns of neural activity, one for each movie condition, that were significantly and reliably correlated between healthy participants. Of the twelve patients who watched the audiovisual movie, 25% produced a pattern of activity that was significantly correlated with the healthy group, while of the ten who listened to the audio narrative, 30% produced electrophysiological patterns similar to controls (one patient responded appropriately to both). The method presented here allows for rapid bedside assessment of higher-order cognitive processing in patients with disorders of consciousness. By leveraging the common neural response to movie stimuli, we were able to identify comparable patterns of brain activity in individual, behaviourally non-responsive patients, reflecting a capacity for narrative processing.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".