The Impact of Concussion, Sport, and Time in Season on Saliva Telomere Length in Healthy Athletes
Bibliographic record
Abstract
To date, sport-related concussion diagnosis and management is primarily based on subjective clinical tests in the absence of validated biomarkers. A major obstacle to clinical validation and application is a lack of studies exploring potential biomarkers in non-injured populations. This cross-sectional study examined the associations between saliva telomere length (TL) and multiple confounding variables in a healthy university athlete population. One hundred eighty-three (108 male and 75 female) uninjured varsity athletes were recruited to the study and provided saliva samples at either pre- or mid-season, for TL analysis. Multiple linear regression was used to determine the associations between saliva TL and history of concussion, sport contact type, time in season (pre vs. mid-season collection), age, and sex. Results showed no significant associations between TL and history of concussion, age, or sport contact type. However, TL from samples collected mid-season were longer than those collected pre-season [β = 231.4, 95% CI (61.9, 401.0), p = 0.008], and males had longer TL than females [β = 284.8, 95% CI (111.5, 458.2), p = 0.001] when adjusting for all other variables in the model. These findings population suggest that multiple variables may influence TL. Future studies should consider these confounders when evaluating saliva TL as a plausible fluid biomarker for SRC.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".