4.3 Analysis of biomarkers following an aerobic exercise intervention for individuals recovering from concussion
Bibliographic record
Abstract
Objective To compare neurotrophic, inflammatory, and axonal injury blood biomarker concentrations between a structured aerobic exercise protocol (SAEP) and usual care exercise protocol (UCEP) groups post-intervention. Design This study was a subset of a longitudinal, non-blinded randomized-controlled trial. Setting Academic Sport Medicine Clinic, University of Toronto. Participants Thirty-nine participants were enrolled in the study of which 13 participants had peripheral blood drawn at 7- and 21-days post-injury (PI). Six were allocated to the SAEP and 7 to the UCEP. Inclusion criteria included a concussion diagnosis by a sports medicine physician within 7-days PI, between 13–25 years old and ability to speak/understand English. Exclusion criteria included prior concussion within two weeks of presenting with concussion, presence of co-morbid injuries and pre-existing health conditions. Interventions (or Assessment of Risk Factors) Participants allocated to the SAEP protocol underwent a stepwise exercise protocol consisting of 8 exercise sessions performed over 11 days progressing in duration and intensity based on participants’ age-predicted maximal heart rate. Outcome Measures Primary outcome measures included concentrations of brain-derived neurotrophic factor, inflammatory cytokines and chemokines, or axonal injury biomarkers. Main Results No significant between-group differences for any of the blood biomarkers examined at 21-days PI were observed. Furthermore, the change in blood biomarker concentrations from 7- to 21-days PI between and within groups did not significantly differ. Conclusions Findings of this study suggest that exercise initiated acutely following concussion does not appear to influence resting levels of peripheral neurotrophic, inflammatory, and axonal injury blood biomarkers. Trial Registration ClinicalTrials.gov, no. NCT02969824
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".