Diffusion Tensor Imaging Findings and Postconcussion Symptom Reporting Six Weeks Following Mild Traumatic Brain Injury
Bibliographic record
Abstract
The purpose of this study is to examine the relation between the microstructural architecture of white matter, as measured by diffusion tensor imaging (DTI), and postconcussion symptom reporting 6-8 weeks following mild traumatic brain injury (MTBI). Participants were 108 patients prospectively recruited from a Level 1 Trauma Center (Vancouver, BC, Canada) following an orthopedic injury [i.e., 36 trauma controls (TCs)] or MTBI (n = 72). DTI of the whole brain was undertaken using a Phillips 3T scanner at 6-8 weeks postinjury. Participants also completed a 5 h neurocognitive test battery and a brief battery of self-report measures (e.g., depression, anxiety, and postconcussion symptoms). The MTBI sample was divided into two groups based on ICD-10 criteria for postconcussional syndrome (PCS): first, PCS-present (n = 20) and second, PCS-absent (n = 52). There were no significant differences across the three groups (i.e., TC, PCS-present, and PCS-absent) for any of the neurocognitive measures (p = .138-.810). For the self-report measures, the PCS-present group reported significantly more anxiety and depression symptoms compared with the PCS-absent and TC groups (p < .001, d = 1.63-1.89, very large effect sizes). For the DTI measures, there were no significant differences in fractional anisotropy, axial diffusivity, radial diffusivity, or mean diffusivity when comparing the PCS-present and PCS-absent groups. However, there were significant differences (p < .05) in MD and RD when comparing the PCS-present and TC groups. There were significant differences in white matter between TC subjects and the PCS-present MTBI group, but not the PCS-absent MTBI group. Within the MTBI group, white-matter changes were not a significant predictor of ICD-10 PCS.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".