Cortical thickness and subcortical brain volumes in professional rugby league players
Bibliographic record
Abstract
The purpose of this study was to examine cortical thickness and subcortical volumes in professional rugby players with an extensive history of concussions compared to control subjects. Participants included 24 active and former professional rugby league players [Age M(SD) = 33.3(6.3); Range = 21–44] with an extensive history of concussion and 18 age- and education-matched controls with no history of neurotrauma or participation in contact sports. Participants underwent T1-weighted imaging and completed a neuropsychological battery, including two tests of memory. Whole brain cortical thickness analysis and structural volume analysis was performed using FreeSurfer version 6.0. Professional rugby league players reported greater alcohol consumption (p < .001) and had significantly worse delayed recall of a visually complex design (p = .04). They did not differ from controls on other clinical outcome measures. There were no differences in cortical thickness between the groups. Professional players had smaller whole brain (p = .003), bilateral hippocampi (ps = .03), and left amygdala volumes (p = .01) compared to healthy controls. Within the players group, there were significant associations between greater alcohol use and smaller bilateral hippocampi and left amygdala volumes. There were no associations between structural volumes and history of concussions or memory performance. The literature examining cortical thickness in athletes with a history of multiple concussions is mixed. We did not observe differences in cortical thickness in professional rugby league players compared to controls. However, smaller subcortical volumes were found in players that were, in part, associated with greater alcohol consumption.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".