Investigating the effect of subconcussive impact exposure on white matter integrity in collegiate football players
Bibliographic record
Abstract
Background: Recently, there has been growing concern around subconcussive impacts (SCI) in football and the potential implications on neurologic health. Of particular interest are athletes who undergo repeated exposure to SCI over a single football season. This form of neurotrauma is often overlooked due to lack of observable symptomatology, however previous work has shown evidence of microstructural damage to white matter (WM). Methods: 22 Canadian Collegiate varsity football players were studied over a single competitive season using accelerometers and diffusion tensor imaging (DTI). Accelerometers were used to assess impacts received by each player throughout the season. Imaging was conducted at 3 timepoints (PRE-season; PRE, post-training camp; PTC, POST-season; POST) and probabilistic tractography was used to delineate WM tracts of interest. Changes in WM microstructure were assessed at each time point and between exposure groups (high exposure; HE, low exposure; LE, based on impacts over the season) using the diffusion metrics fractional anisotropy (FA) and mean diffusivity (MD). Results: The HE group had significantly lower FA in the left and right corticospinal tracts (CST), left anterior thalamic radiation (ATR), and left superior thalamic radiation (STR), and higher MD in the left and right CST, left ATR, and left inferior fronto-occipital fasciculus (IFO) compared to the LE group. FA was found to decrease in the LE group from PRE and PTC to POST timepoints in the right posterior thalamic radiation (PTR) and right CST respectively. The HE group had decreased FA from PTC to POST in forceps minor (FMI). MD was found to be significantly decreased from PRE to PTC in the right superior longitudinal fasciculus (SLF) in the HE group. Conclusions: Our findings indicate changes to WM microstructure occurs silently due to repetitive SCI exposure which may persist beyond a single football season.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".