Diffusion tensor imaging of sport related concussion in adolescents
Bibliographic record
Abstract
Objectives To establish the short-term changes in white matter integrity following sport related concussion in adolescents and to examine the association between changes in white matter integrity and a clinical measure of concussion. Design Cross-sectional within cohort study. Setting University hospital and community ice hockey arenas. Subjects Twelve adolescents with sport related concussion (ages 14–17 years) within 2 months of injury and 10, active adolescents with no previous history of concussion. Assessment of Risk Factors Adolescents within 2 months of sport-related concussion and healthy adolescents with no history of concussion were assessed using the Sport Concussion Assessment Tool 2 (SCAT2) and total SCAT2 score (/100) was considered. Outcome Measures Two measures of diffusion tensor imaging (DTI): fractional anisotropy (FA) and mean diffusivity (MD). Results Whole brain FA values were significantly increased (F (1,40)=6.29, p=0.01) and MD values decreased (F(1,40)=4.75, p=0.036) in concussed athletes compared with control participants. SCAT2 total scores were associated with whole brain FA and MD values with lower scores associated with higher FA (R2=0.25, p=0.017) and lower MD (R2=0.2, p=0.038). Conclusion This preliminary study provides evidence of microstructural changes in the integrity of the white matter in adolescent athletes following a sport related concussion. In addition, we found a relationship between measures of white matter integrity and the SCAT2 up to 16–61 days following concussion, which may indicate persistent structural change in the adolescent brain after injury. Further study should chart the trajectory of brain injury and recovery in this population. Acknowledgements Martha Piper Research Fund, University of British Columbia and the Brain Research Centre, University of British Columbia. Competing interests None.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".