Serum neurofilament light concentration increases following a season of american football
Bibliographic record
Abstract
Objective To examine neurofilament light (NF-L), a microtubule-associated protein widely regarded to be central nervous system (CNS) specific, concentrations in serum prior to and following a season of contact (American football) and non-contact (cross-country running) in elite, collegiate-aged athletes. Design Prospective cohort. Setting Laboratory. Participants 13 male American football athletes (19±1.2 years) and 11 cross-country runners (20±1.9 years). Intervention Pre- and post-season blood samples were obtained from all participants and spun at 2000 g for 10 minutes to extract serum. NF-L was detected through the isolation of individual immunocomplexes on pragmatic beads using standard ELISA reagents. The beads were then trapped in single-molecule, femtolitre-sized wells, allowing for a digital readout of each individual bead. The digital nature of this technique allows an average of 1000x sensitivity increase over conventional assays. Outcome measures Analysis of pre- and post-season serum samples provide NF-L concentrations. Results Serum samples from American football players showed a significant increase (p=0.04) from pre-season NF-L serum concentrations (95% CI: 6.51 – 9.19 pg/mL) to post-season (95% CI: 7.08 – 11.70 pg/mL). Serum NF-L concentrations in cross-country runners showed no change (p=0.57) from pre-season (95% CI: 5.05 – 6.94 pg/mL) to post-season (95% CI: 5.05 – 6.88 pg/mL). Conclusions Experiencing repetitive subconcussive head-trauma throughout a season of American football elevates serum NF-L concentrations. This finding indicates multiple head-impacts may lead to CNS axonal damage. Competing interests None.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".