Neurofilament‐light in former athletes: a potential biomarker of neurodegeneration and progression
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: This study aimed to evaluate serum neurofilament light chain (NF-L) levels in former professional contact sports athletes with multiple concussions (ExPro) as a potential biomarker of neurodegeneration and predictor of white-matter (WM) abnormality progression. METHODS: Concentrations of NF-L in the serum of fifty-two cognitively normal ExPro and twenty-one healthy controls (HC) with no history of concussions were measured using single molecule array (Simoa) technology. Both groups underwent neuroimaging at the time of serum collection. Eighteen of the participants in the ExPro underwent follow-up imaging after 2 years. RESULTS: Levels of serum NF-L were not significantly different between the ExPro and HC. However, in the ExPro group, NF-L levels were positively correlated with the mean diffusivity (MD) of corpus callosum (CC) and fornix, and total ventricular volume. Moreover, NF-L levels in the ExPro group at the first visit were positively correlated with the amount of increase in CC MD at the 2-year follow-up. CONCLUSIONS: NF-L levels reflect neuronal changes in the ExPro group and predict the extent of decrease in white matter integrity over time. Serum NF-L might be a biomarker of neurodegeneration and WM abnormality progression in ExPro.
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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 | 0.001 | 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".