The relationship between symptom burden and systemic inflammation differs between male and female athletes following concussion
Bibliographic record
Abstract
BACKGROUND: Inflammation appears to be an important component of concussion pathophysiology. However, its relationship to symptom burden is unclear. Therefore, the purpose of this study was to evaluate the relationship between symptoms and inflammatory biomarkers measured in the blood of male and female athletes following a sport-related concussion (SRC). RESULTS: Forty athletes (n = 20 male, n = 20 female) from nine interuniversity sport teams at a single institution provided blood samples within one week of an SRC. Twenty inflammatory biomarkers were quantitated by immunoassay. The Sport Concussion Assessment Tool version 5 (SCAT-5) was used to evaluate symptoms. Partial least squares (PLS) analyses were used to evaluate the relationship(s) between biomarkers and symptoms. In males, a positive correlation between interferon (IFN)-γ and symptom severity was observed following SRC. The relationship between IFN-γ and symptoms was significant among all symptom clusters, with cognitive symptoms displaying the largest effect. In females, a significant negative relationship was observed between symptom severity and cytokines IFN-γ, tumor necrosis factor (TNF)-α, and myeloperoxidase (MPO); a positive relationship was observed between symptom severity and MCP-4. Inflammatory mediators were significantly associated with all symptom clusters in females; the somatic symptom cluster displayed the largest effect. CONCLUSION: These results provide supportive evidence of a divergent relationship between inflammation and symptom burden in male and female athletes following SRC. Future investigations should be cognizant of the potentially sex-specific pathophysiology underlying symptom presentation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".