Unusual combinations of neurodegenerative pathologies with chronic traumatic encephalopathy (<scp>CTE</scp>) complicates clinical prediction of <scp>CTE</scp>
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Chronic traumatic encephalopathy (CTE) has gained widespread attention due to its association with multiple concussions and contact sports. However, CTE remains a postmortem diagnosis, and the link between clinical symptoms and CTE pathology is poorly understood. This study aimed to investigate the presence of copathologies and their impact on symptoms in former contact sports athletes. METHODS: This was a retrospective case series design of 12 consecutive cases of former contact sports athletes referred for autopsy. Analyses are descriptive and include clinical history as well as the pathological findings of the autopsied brains. RESULTS: All participants had a history of multiple concussions, and all but one had documented progressive cognitive, psychiatric, and/or motor symptoms. The results showed that 11 of the 12 participants had evidence of CTE in the brain, but also other copathologies, including different combinations of tauopathies, and other rare entities. CONCLUSIONS: The heterogeneity of symptoms after repetitive head injuries and the diverse pathological combinations accompanying CTE complicate the prediction of CTE in clinical practice. It is prudent to consider the possibility of multiple copathologies when clinically assessing patients with repetitive head injuries, especially as they age, and attributing neurological or cognitive symptoms solely to presumptive CTE in elderly patients should be discouraged.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".