Absence of chronic traumatic encephalopathy in retired football players with multiple concussions and neurological symptomatology
Bibliographic record
Abstract
BACKGROUND: Chronic traumatic encephalopathy (CTE) is the term coined for the neurodegenerative disease often suspected in athletes with histories of repeated concussion and progressive dementia. Histologically, CTE is defined as a tauopathy with a distribution of tau-positive neurofibrillary tangles (NFTs) that is distinct from other tauopathies, and usually shows an absence of beta-amyloid deposits, in contrast to Alzheimer's disease (AD). Although the connection between repeated concussions and CTE-type neurodegeneration has been recently proposed, this causal relationship has not yet been firmly established. Also, the prevalence of CTE among athletes with multiple concussions is unknown. METHODS: We performed a consecutive case series brain autopsy study on six retired professional football players from the Canadian Football League (CFL) with histories of multiple concussions and significant neurological decline. RESULTS: All participants had progressive neurocognitive decline prior to death; however, only 3 cases had post-mortem neuropathological findings consistent with CTE. The other 3 participants had pathological diagnoses of AD, amyotrophic lateral sclerosis (ALS), and Parkinson's disease (PD). Moreover, the CTE cases showed co-morbid pathology of cancer, vascular disease, and AD. DISCUSSION: Our case studies highlight that not all athletes with history of repeated concussions and neurological symptomology present neuropathological changes of CTE. These preliminary findings support the need for further research into the link between concussion and CTE as well as the need to expand the research to other possible causes of taupathy in athletes. They point to a critical need for prospective studies with good sampling methods to allow us to understand the relationship between multiple concussions and the development of CTE.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".