The impact of fitness level on self-report of concussion symptoms
Bibliographic record
Abstract
Background The diagnosis and management of concussion in sport rely heavily on self-report of symptoms by the athlete. However, many symptoms commonly reported after a concussion (headache, nausea, fatigue, etc.) may be influenced by other factors. Fatigue is a frequent complaint, but may actually be a function of level of physical fitness. Objective To evaluate the role of physical fitness on self-report of concussion symptoms in collegiate athletes and students, during baseline testing. Design Prospective repeated measures. Participants 125 subjects were recruited, including 95 collegiate athletes and 30 undergraduate students (83 males and 42 females). Athletes were screened for medical and psychological conditions. No athlete had a recent history of concussion. Intervention Subjects completed the Standardised Concussion Assessment Tool (SCAT1) at three time periods: prior to a Leger (Beep) test, within 10 min of test completion, and within 24 h. The Leger test has established validity and reliability to estimate an athlete's V02 maximum and overall fitness levels. Main outcome measures Estimated V02 max (Leger test) and symptom scores on the SCAT1. Results Subjects were grouped into three levels of fitness according to criteria established by the American College of Sports Medicine (2010). A 3×3 repeated-measures ANOVA was not significant for the overall model but showed a significant interaction between time and fitness level (F (2, 121)=3.50, p=0.02). Post-hoc analysis revealed significant differences in report of symptoms among the three fitness groups at baseline and immediately post-activity, but not at 24 h. Conclusion Results provide evidence of a moderating effect of fitness level on report of concussion symptoms at baseline, even in healthy adults. Specifically, exercise can induce symptom reporting, and may a function of an athlete's level of conditioning. Sport medicine professionals making decisions following concussion need to consider an athlete's level of fitness when evaluating post-concussion symptoms.
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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 | 0.000 | 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) | 0.002 | 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".