Chronic Traumatic Encephalopathy in Professional American Football Players: Where Are We Now?
Bibliographic record
Abstract
Repetitive head trauma provides a favorable milieu for the onset of inflammatory and neurodegenerative processes. The result of long-lasting head trauma is chronic traumatic encephalopathy (CTE), a disease process well-recognized in boxers, military personnel, and more recently, in American football players. CTE is a chronic neurodegenerative disease with hallmarks of hyperphosphorylated tau (p-tau) aggregates and intercellular lesions of neurofibrillary tangles. The criteria for CTE diagnosis requires at least 1-2 focal perivascular lesions of p-tau in the cerebral cortex, at the depth of the sulci. These pathognomonic lesions aggregate within neurons and glial cells such as astrocytes, and cell processes within the vicinity of small blood vessels. CTE presents in a distinct topographical distribution pattern compared to other tauopathies such as AD and other age-related astrogliopathies. CTE also has an insidious onset, years after repetitive head trauma. The disease course of CTE is characterized by cognitive dysfunction, behavioral changes, and can progress to altered motor function with parkinsonian-like manifestations in later stages. This short review aims to summarize CTE in professional football, epidemiology, diagnosis based on neuroanatomical abnormalities, cognitive degeneration, and adverse mental health effects, as well as gaps in the literature and future directions in diagnostics, therapeutics, and preventive measures.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".