2.16 Blood biomarkers for sport-related concussion: considering the effects of interval exercise and matrix differences
Bibliographic record
Abstract
Objective Examine plasma t-tau, NFL, and GFAP levels in serial samples following interval exercise, while considering age, sex, and sleep, and assess agreement between plasma and serum biomarker concentrations. Design Randomized-crossover cohort. Setting UCalgary Laboratory. Participants Ten healthy participants were recruited from the UCalgary community (ages 19–26; 7:3/female:male). Participants excluded if prior diagnosed concussion or metabolic disease, not recreationally active, and/or a daily smoker. Interventions (or Assessment of Risk Factors) Three differing intensity 30-minute interval exercise conditions on a cycle ergometer separated by one month in randomized order (control/rest, moderate intensity interval training (MIIT), high intensity interval training (HIIT)). Outcome Measures Plasma and serum concentrations of t-tau, NFL, and GFAP at serial timepoints (pre, during, and 0, 1, 2, 4, 6, 8, 24, 48 hours following). Main Results Compared to control, plasma NFL and GFAP decreased immediately following completion of MIIT (NFL;β=-1.002,95%CI:-1.852--0.152,p=0.021. GFAP;β=-14.750,95%CI:-27.154--2.345,p=0.020) and HIIT (NFL;β=-1.414,95%CI:-2.263--0.564,p=0.001. GFAP;β=-20.956,95%CI:-33.358--8.554,p=0.001). More sleep was associated with lower plasma NFL (β=-0.003,95%CI:-0.005--0.001,p=0.012). Bland-Altman plots showed 1.36 pg/mL (95%LOA:-0.77–3.49) greater t-tau in plasma than serum, and 1.54 pg/mL (95%LOA:-4.16–1.07) less NFL and 11.80 pg/mL (95%LOA:-43.46–19.86) less GFAP in plasma than serum. Conclusions Plasma t-tau appeared unaffected by MIIT and HIIT. Short-lived NFL and GFAP decreases along with poor agreement between plasma and serum concentrations suggests exercise influences and matrix differences are pertinent considerations for clinical validation of blood biomarkers associated with acute sport-related concussion. Future studies investigating influence of sleep are warranted.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".