Effect of Player Position on Serum Biomarkers during Participation in a Season of Collegiate Football
Bibliographic record
Abstract
This prospective cohort study examined the relationship between a panel of four serum proteomic biomarkers (glial fibrillary acidic protein [GFAP], ubiquitin C-terminal hydrolase-L1 [UCH-L1], total Tau, and neurofilament light chain polypeptide [NF-L]) in 52 players from two different cohorts of male collegiate student football athletes from two different competitive seasons of Division I National Collegiate Athletic Association Football Bowl Subdivision. This study evaluated changes in biomarker concentrations (as indicators of brain injury) over the course of the playing season (pre- and post-season) and also assessed biomarker concentrations by player position using two different published classification systems. Player positions were divided into: 1) speed (quarterbacks, running backs, halfbacks, fullbacks, wide receivers, tight ends, defensive backs, safety, and linebackers) versus non-speed (offensive and defensive linemen), and 2) “Profile 1” (low frequency/high strain magnitudes positions including quarterbacks, wide receivers, and defensive backs), “Profile 2” (mid-range impact frequency and strain positions including linebackers, running backs, and tight ends), and “Profile 3” (high frequency/low strains positions including defensive and offensive linemen). There were significant increases in GFAP 39.3 to 45.6 pg/mL and NF-L 3.5 to 5.4 pg/mL over the course of the season ( p < 0.001) despite only five players being diagnosed with concussion. UCH-L1 decreased significantly, and Tau was not significantly different. In both the pre- and post-season blood samples Tau and NF-L concentrations were significantly higher in speed versus non-speed positions. Concentrations of GFAP, Tau, and NF-L increased incrementally from “Profile 3,” to “Profile 2” to “Profile 1” in the post-season. UCH-L1 did not. GFAP increased (by Profiles 3, 2, 1) from 42.4 to 49.6 to 78.2, respectively ( p = 0.051). Tau increased from 0.37 to 0.61 to 0.67, respectively ( p = 0.024). NF-L increased from 3.5 to 4.9 to 8.2, respectively ( p < 0.001). Although GFAP and Tau showed similar patterns of elevations by profile in the pre-season samples they were not statistically significant. Only NF-L showed significant differences between profiles 2.7 to 3.1 to 4.2 in the pre-season ( p = 0.042). GFAP, Tau, and NF-L concentrations were significantly associated with different playing positions with the highest concentrations in speed and “Profile 1” positions and the lowest concentrations were in non-speed and “Profile 3” positions. Blood-based biomarkers (GFAP, Tau, NF-L) provide an additional layer of injury quantification that could contribute to a better understanding of the risks of playing different positions.
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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) | 0.001 | 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".