Structural brain injury in patients with disorders of consciousness: A voxel-based morphometry study
Bibliographic record
Abstract
MAIN OBJECTIVE: Disorders of consciousness (DOC; encompassing coma, vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state minus/plus (MCS-/+)) are associated with structural brain injury. The extent of this damage remains poorly understood and merits a detailed examination using novel analysis techniques. Research design/methods and procedures: This study used voxel-based morphometry (VBM) on structural magnetic resonance imaging scans of 61 patients with DOC to examine grey and white matter injury associated with DOC, time spent in DOC, aetiology and diagnosis. MAIN OUTCOMES AND RESULTS: DOC and time spent in DOC were found to be associated with widespread structural brain injury, although the latter did not correlate strongly with injury in the right cerebral hemisphere. Traumatic, as compared to non-traumatic aetiology, was related to more injury in the brainstem, midbrain, thalamus, hypothalamus, basal forebrain, cerebellum, and posterior corpus callosum. Potential structural differences were found between VS/UWS and MCS and between MCS- and MCS+, but need further examination. CONCLUSIONS: The findings indicate that both traumatic and non-traumatic DOC are associated with widespread structural brain injury, although differences exist that could lead to aetiology-specific treatment strategies. Furthermore, the high degree of atrophy occurring after initial brain injury prompts the development and use of neuroprotective techniques to potentially increase patients' chances of recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".