3.25 Feasibility of the functional exertional test for concussion: a pilot study
Bibliographic record
Abstract
Objective To implement the use of the functional exertional test for concussion and identify normal responses in a healthy population. Design Prospective observational cohort (pilot) study. Setting University. Participants 14 Canadian interuniversity athletes enrolled in and completed the study [female n=8, male n=6]). Interventions Functional exertional tasks: 1) aerobic exercise, 2) exercises grounded in physical literacy with head movement, 6) multi-tasks combining physical and cognitive components. Independent variables: Successful or unsuccessful completion of the test. Outcome Measures Symptom exacerbation, heart rate (HR), repetitions, and cognitive scores. Main Results All 14 participants successfully completed the functional exertional test with no exacerbation of concussion symptoms. The HR of 10 athletes was available for analysis and significant elevation in HR was identified during the exertional tasks compared to rest. The mean differences were: aerobic exercise stage = 18.62 beats per minute (bpm) (95% CI 4.32–32.92; p=0.01), head movement stage = 18.55 bpm (95% CI 2.81–34.29; p=0.02), and multi-tasks stage = 30.11 bpm (95% CI 13.74–46.47; p=0.001). Conclusions Preliminary findings suggest that the functional exertional test for concussion does not elicit substantial symptom exacerbation, while increasing athletes’ HR during specific tasks with head acceleration, cognitive/sensory stimuli and decision making, and can successfully be completed by a healthy population. Future research will be conducted with a larger healthy population as well as with athletes with concussion to determine if the test is valid for concussion management.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".