The Toronto traumatic brain injury study
Bibliographic record
Abstract
OBJECTIVE: To assess the relationship between regional brain volume changes and traumatic brain injury (TBI) severity in patients with and without focal lesions. METHODS: Sixty-nine chronic-phase TBI patients spanning the full range of severity were recruited from consecutive hospital admissions. Patients received high-resolution structural MRI a minimum of 1 year after injury. Multivariate statistical analyses assessed covariance patterns between volumes of gray matter, white matter, and sulcal/subdural and ventricular CSF across 38 brain regions and TBI severity as assessed by depth of coma at the time of injury. Patients with diffuse and diffuse plus focal injury were analyzed both separately and together. RESULTS: There was a stepwise, dose-response relationship between parenchymal volume loss and TBI severity. Patients with moderate and severe TBI were differentiated from those with mild TBI, who were in turn differentiated from noninjured control subjects. A spatially extensive pattern of volume loss covaried with TBI severity, with particularly widespread effects in white matter volume and sulcal/subdural CSF. The most reliable effects were observed in the frontal, temporal, and cingulate regions, although effects were observed to varying degrees in nearly every brain region. Focal lesions were associated with greater volume loss in frontal and temporal regions, but volume loss remained marked even when analyses were restricted to patients with diffuse injury. CONCLUSIONS: Patterns of parenchymal volumetric changes can differentiate among levels of traumatic brain injury (TBI) severity, even in mild TBI. TBI causes a spatially extensive pattern of volume loss that reflects independent but overlapping contributions of focal and diffuse injury.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".