White Matter Abnormalities in Retired Professional Rugby League Players with a History of Concussion
Bibliographic record
Abstract
The topic of potential long-term neurological consequences from having multiple concussions during a career in collision sports is controversial. We sought to investigate white matter microstructure using diffusion tensor imaging (DTI) in retired professional Australian National Rugby League (NRL) players ( n = 11) with a history of multiple self-reported concussions compared with age- and education-matched controls ( n = 13) who have had no history of brain trauma. Diffusion-weighted images were acquired with a Siemens 3T scanner. All participants completed a clinical interview. There were no significant differences between groups on measures of depression, anxiety, stress, or post-concussion symptoms; however, NRL players scored significantly higher on the alcohol use disorder identification test (AUDIT). Voxelwise analyses of DTI measures were performed using tract-based spatial statistics (TBSS) with age and AUDIT scores included as covariates. TBSS revealed significantly reduced fractional anisotropy (FA), and increased radial diffusivity (RD), axial diffusivity (AD), and trace (TR) in white matter regions of recently retired NRL players compared with controls. FA was significantly reduced in the right superior longitudinal fasciculus and right corticospinal tract while TR, RD, and AD were increased in these regions, as well as the corpus callosum, forceps major, right uncinate fasciculus, and left corticospinal tract. In summary, DTI in a small cohort of recently retired professional NRL players with a history of multiple concussions showed differences in white matter microstructure compared with age- and education-matched controls with no history of brain trauma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".