The Effect of Physical Exertional Testing on Postconcussion Symptom Scale Scores in Male and Female High School Students
Bibliographic record
Abstract
PURPOSE: Symptom scores commonly measured following concussion were compared between male and female adolescents with (Hx+) and without (Hx-) a history of concussion, pre and post physical exertion testing. METHODS: Eighty (males [n = 60; Hx+ = 19], female [n = 20; Hx+ = 5]) high school students (ages 15-17 y) completed the Buffalo Concussion Treadmill Test once and the modified shuttle run test twice. Symptom scores were collected using the 22-point Symptom Evaluation Scale on the Sport Concussion Assessment Tool (version 5) immediately pre and post physical exertion testing. RESULTS: The symptoms most reported during preexertional testing were fatigue/low energy, feeling slowed down, and nervous/anxious, whereas feeling slowed down, fatigue/low energy, "pressure in head" (males only), and headache (females only) were most frequently reported during postexertion testing. CONCLUSION: An understanding of the common exertion-related symptoms at baseline in a laboratory or field-based setting in adolescents may be advantageous for clinicians as they manage individual recovery postconcussion. This is particularly important during an adolescent's recovery and return to play when exertional testing may be implemented, especially since symptoms were reported pre and post exertional testing in both males and females regardless of concussion history.
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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) | 0.000 | 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".