Diffusion-Weighted Magnetic Resonance Imaging Improves Outcome Prediction in Adult Traumatic Brain Injury
Bibliographic record
Abstract
In patients with traumatic brain injury (TBI), diffuse axonal injury (DAI) accounts for a significant amount of parenchymal injury. Diffusion weighted magnetic resonance imaging (DWI) is known to be sensitive for detecting visible DAI lesions. We focused on detection of non-visible, quantifiable diffusion changes in specific normal-appearing brain regions, using apparent diffusion coefficient (ADC) maps. Thirty-seven adults with TBI were compared to 35 age-matched control patients. DWI was performed and ADC maps were generated. Thirty-one regions of interest (ROI) were manually drawn on ADC maps and ADC values extracted. Brain ROIs were categorized into five zones: peripheral gray matter, peripheral white matter, deep gray matter, deep white matter, and posterior fossa. ADC results were compared with the severity of injury based on the admission Glasgow Coma Scale (GCS 3-8; severe; GSC 9-15 mild/moderate) and with long-term outcome (6-12 months after injury) using the Glasgow Outcome Scale (GOS 1-3, unfavorable; GOS: 4-5, favorable) score. Mean ADC values in all five brain zones were significantly different between TBI subjects and controls (p<or=0.05). Patients with more severe injury (i.e., GCS 3-8) had significantly different mean ADC values than control patients (p<or=0.05) and patients with unfavorable outcomes had significantly higher mean deep gray and white matter ADC values compared to those with favorable outcomes and to controls (p<or=0.05). Thus, ADC maps can be used to detect non-visible DAI lesions. In addition, injuries in the deep gray and white matter may be useful to predict outcome in adult TBI patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".