Hockey Concussion Education Project, Part 3. White matter microstructure in ice hockey players with a history of concussion: a diffusion tensor imaging study
Bibliographic record
Abstract
OBJECT: The aim of this study was to examine the brain's white matter microstructure by using MR diffusion tensor imaging (DTI) in ice hockey players with a history of clinically symptomatic concussion compared with players without a history of concussion. METHODS: Sixteen players with a history of concussion (concussed group; mean age 21.7 ± 1.5 years; 6 female) and 18 players without a history of concussion (nonconcussed group; mean age 21.3 ± 1.8 years, 10 female) underwent 3-T DTI at the end of the 2011-2012 Canadian Interuniversity Sports ice hockey season. Tract-based spatial statistics (TBSS) was used to test for group differences in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and the measure "trace," or mean diffusivity. Cognitive evaluation was performed using the Immediate Postconcussion Assessment and Cognitive Test (ImPACT) and the Sport Concussion Assessment Tool-2 (SCAT2). RESULTS: TBSS revealed a significant increase in FA and AD, and a significant decrease in RD and trace in several brain regions in the concussed group, compared with the nonconcussed group (p < 0.05). The regions with increased FA and decreased RD and trace included the right posterior limb of the internal capsule, the right corona radiata, and the right temporal lobe. Increased AD was observed in a small area in the left corona radiata. The DTI measures correlated with neither the ImPACT nor the SCAT2 scores. CONCLUSIONS: The results of the current study indicate that a history of concussion may result in alterations of the brain's white matter microstructure in ice hockey players. Increased FA based on decreased RD may reflect neuroinflammatory or neuroplastic processes of the brain responding to brain trauma. Future studies are needed that include a longitudinal analysis of the brain's structure and function following a concussion to elucidate further the complex time course of DTI changes and their clinical meaning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".