Perceptual-cognitive three-dimensional multiple-object tracking task can help the monitoring of sport-related concussion
Bibliographic record
Abstract
OBJECTIVES: While the rate of sport-related concussion is increasing, more effective tools are needed to help monitor the diagnosis and return to play of athletes. The three-dimensional multiple-object tracking (3D-MOT) exercise is a perceptual-cognitive task that has shown predictive power towards the dynamic requirements of real-world activities such as sport. This study introduced the use of the 3D-MOT task, along with the Standardized Assessment of Concussion (SAC) and Modified Balance Error Scoring System (M-BESS) tests, for diagnosis and return to play in professional sports. METHODS: Fifty-nine professional athletes were tested with the 3D-MOT, SAC and M-BESS tests at 48 hours following the injury. The same measures were employed to evaluate the return to play following the standard concussion management protocol. The SAC and M-BESS tests were also performed in pre-season (baseline) in 32 out of the 59 athletes. RESULTS: The injured athletes exhibited poor performance on 3D-MOT at 48 hours post injury compared with return to play (p<0.001) as well as compared with healthy professionals' performance scores (p<0.001). Importantly, learning rate, which participants are thought to have an expert advantage on this perceptual-cognitive task, was totally disrupted at 48 hours post injury compared with healthy professionals (p<0.001). The 3D-MOT performance was also correlated to the total number of symptoms (p=0.020), SAC (p=0.031) and M-BESS (p=0.004) scores at 48 hours. Not surprisingly, SAC and M-BESS tests' usefulness for monitoring concussion was found to be weak, particularly when test performance following the injury was compared to baseline (p=0.056 and 0.349 for SAC and M-BESS, respectively). CONCLUSION: 3D-MOT could help monitor sport-related concussion in professional athletes. The discussion also covers the critical importance of perceptual-cognitive assessment following concussion in the athletic population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".