Relationship between etiology and covert cognition in the minimally conscious state
Bibliographic record
Abstract
OBJECTIVES: Functional neuroimaging has shown that the absence of externally observable signs of consciousness and cognition in severely brain-injured patients does not necessarily indicate the true absence of such abilities. However, relative to traumatic brain injury, nontraumatic injury is known to be associated with a reduced likelihood of regaining overtly measurable levels of consciousness. We investigated the relationships between etiology and both overt and covert cognitive abilities in a group of patients in the minimally conscious state (MCS). METHODS: Twenty-three MCS patients (15 traumatic and 8 nontraumatic) completed a motor imagery EEG task in which they were required to imagine movements of their right-hand and toes to command. When successfully performed, these imagined movements appear as distinct sensorimotor modulations, which can be used to determine the presence of reliable command-following. The utility of this task has been demonstrated previously in a group of vegetative state patients. RESULTS: Consistent and robust responses to command were observed in the EEG of 22% of the MCS patients (5 of 23). Etiology had a significant impact on the ability to successfully complete this task, with 33% of traumatic patients (5 of 15) returning positive EEG outcomes compared with none of the nontraumatic patients (0 of 8). CONCLUSIONS: The overt behavioral signs of awareness (measured with the Coma Recovery Scale-Revised) exhibited by nontraumatic MCS patients appear to be an accurate reflection of their covert cognitive abilities. In contrast, one-third of a group of traumatically injured patients in the MCS possess a range of high-level cognitive faculties that are not evident from their overt behavior.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".