Does an Individual's Fitness Level Affect Baseline Concussion Symptoms?
Bibliographic record
Abstract
CONTEXT: Variables that may influence baseline concussion symptoms should be investigated. OBJECTIVE: To evaluate the effect of physical fitness on self-report of baseline concussion symptoms in collegiate athletes and students. DESIGN: Controlled laboratory study. PATIENTS OR OTHER PARTICIPANTS: A total of 125 undergraduates, including 95 collegiate athletes and 30 recreational athletes (83 males, 42 females). INTERVENTION(S): Participants completed the Standardized Concussion Assessment Tool 2 (SCAT2; symptom report) at baseline, within 10 minutes of completing the Leger test, and within 24 hours of the initial baseline test. The Leger (beep) test is a shuttle-run field test used to predict maximal aerobic power. MAIN OUTCOME MEASURE(S): The total symptom score on the SCAT2 was calculated and analyzed with a repeated-measures analysis of variance. A linear regression analysis was used to determine if 3 variables (sport type, sex, or fitness level) accounted for a significant amount of the variance in the baseline symptom report. RESULTS: Participants reported more symptoms postactivity but fewer symptoms at 24 hours compared with baseline, representing a time effect in our model (F2,234 = 47.738, P < .001). No interactions were seen among the independent variables. We also found an effect for fitness level, with fitter individuals reporting fewer symptoms at all 3 time intervals. The regression analysis revealed that fitness level accounted for a significant amount of the variance in SCAT2 symptoms at baseline (R (2) = 0.22, F3,121 = 11.44, P < .01). CONCLUSIONS: Fitness level affected the baseline concussion symptom report. Exercise seems to induce concussion symptom reporting, and symptom severity may be a function of an athlete's level of conditioning. Sports medicine professionals should consider an athlete's level of fitness when conducting baseline concussion symptom assessments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".