fMRI for vegetative and minimally conscious states
Bibliographic record
Abstract
A more balanced perspective The BBC’s Panorama programme The Mind Reader: Unlocking My Voice broadcast on 13 November 2012 provided important insights into the devastating experience of patients who live in vegetative or minimally conscious states and the families who support them. It also provided useful information on the use of functional magnetic resonance imaging (fMRI) to explore evidence of localised brain activity that might indicate underlying awareness. However, the programme failed to distinguish clearly between the two states and gave the impression that 20% of patients in a vegetative state show cognitive responses on fMRI. This claim needs to be clarified and put into perspective. There are important differences between the two states. Patients in a vegetative state have no discernible awareness of self and no cognitive interaction with their environment. Patients in a minimally conscious state show evidence of interaction through localising or discriminating behaviours, although such interactions occur inconsistently. It is clinically important to make this distinction, for prognostic reasons and because some evidence suggests that patients in a minimally conscious state experience symptoms (such as pain) in a manner indistinguishable from non-brain injured patients.1 2 The programme presented two patients said to be in a “vegetative state” who showed evidence of cognitive interaction on assessment using fMRI in Ontario, Canada. The clinical methods used for the original diagnosis …
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.025 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".