Elevated cerebrospinal fluid total tau in former professional athletes with multiple concussions
Bibliographic record
Abstract
Objective To identify CSF biomarkers that are related to decreased white matter (WM) integrity and poor cognitive performance in former professional athletes with a history of multiple concussions. Methods Concentrations of phosphorylated tau181, total tau (t-tau), and β-amyloid in the CSF were measured in 3 groups: 22 former professional athletes with multiple concussions (mean ± SD age 55.9 ± 12.2 years), 5 healthy controls (age 57.4 ± 5.2 years), and 12 participants (age 60.0 ± 6.6 years) diagnosed with Alzheimer disease (AD). All participants in the former athletes group underwent diffusion tensor imaging to determine WM tract integrity and completed neuropsychological testing. We divided the former athletes group into those with normal (<300 pg/mL) and high (>300 pg/mL) CSF t-tau. Results CSF t-tau in the former athletes group was significantly higher than in the healthy control group (349.3 ± 182.6 vs 188.8 ± 39.9 pg/mL, p = 0.003) and significantly lower than in the patients with AD (349.3 ± 182.6 vs 857.0 ± 449.3 pg/mL, p = 0.007). Fractional anisotropy values across all the tracts were significantly lower in the high CSF t-tau group compared to the normal CSF t-tau group (p = 0.036). Participants in the high CSF t-tau group scored significantly lower on the Trail Making Test (TMT) Part B compared to the normal CSF t-tau group (t scores 45.6 ± 18.8 vs 62.3 ± 10.1, p = 0.017). Conclusion Our findings indicate that former athletes with multiple concussions are at increased risk of elevated levels of CSF t-tau and that high CSF t-tau is associated with reduced WM integrity and worse scores on the TMT Part B.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".