Development and Validation of a Novel Multimodal Exertional Test for Concussion Assessment
Bibliographic record
Abstract
Concussion management is consistently evolving, with exertional testing playing an increasingly important role in return-to-sport decisions. The purpose of this thesis was to develop, evaluate, and implement a multimodal exertional test (MET) to support clinical decision making in concussion care. The first study focused on the development and pilot testing of a structured four-stage, twelve-task MET that progressively increased in complexity: Stage 1) introduced cardiovascular load, Stage 2) incorporated head acceleration, Stage 3) added cognitive tasks, and Stage 4) involved balance and multi-plane movements. Pilot testing with healthy athletes demonstrated that the MET elicited appropriate increases in heart rate, without provoking symptoms, suggesting the MET is both physiologically demanding and well-tolerated. The second study assessed the test-retest reliability of the MET in healthy athletes across two sessions. Results indicated good reliability across symptom reporting, physiological responses, and performance metrics. Improvements in certain task completion times were observed in the second session, likely reflecting learning effects. The third and final study examined the validity and prognostic ability of the MET by comparing performance between healthy athletes and athletes with concussion. Athletes with concussion reported greater symptom provocation and were more likely to fail in completing all tasks, whereas all healthy athletes completed the MET without symptom exacerbation. Additionally, based on MET performance during the first week of injury, athletes with concussion who successfully passed all twelve tasks had a shorter recovery length in comparison to athletes who failed. Altogether, these studies provided the foundational evidence supporting the MET as a reliable, valid, and feasible tool for clinical concussion assessment. The MET offers a structured and scalable approach to exertional testing and may aid in the readiness to return-to-sport, advancing best practices in 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".