Neurobiological and connectivity changes after sports-related concussion
Bibliographic record
Abstract
Traumatic brain injury (TBI) is one of the leading causes of morbidity and mortality worldwide. Sports-related TBI is a subset that encompasses cerebral concussion and chronic traumatic encephalopathy (CTE), the latter of which is a long-term neurodegenerative sequela of repeated mild TBI that affects behaviour, cognition, motor control, and memory. On a cellular level, TBI can result in diffuse axonal injury (DAI). This injury causes axonal transport dysfunction, leading to accumulation of tau and amyloid beta deposits in the brain. Damage occurs in neuronal tracts of both local and distant brain regions. DAI disrupts brain network function, which correlates with decreased cognitive function, by impairing the default mode network’s (DMN) normal ability to deactivate during cognitive tasks. The salience network (SN) can be affected by DAI as well, which ultimately also impairs deactivation of the DMN. These changes coincide with the clinical manifestations of concussions and CTE. Both concussions and CTE are currently clinical diagnoses, as no diagnostic lab tests exist to delineate these conditions. As with many brain disorders of traumatic origin, there is no specific medical treatment for these conditions, though concussion is managed through physical and cognitive rest. The most important consideration for all TBIs, however, is prevention.
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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) | 0.003 | 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".