Novel Pre-Season Concussion Baseline Assessment Including Protocols based on Recognizable Clinically-Relevant Neurosensory Methodologies
Bibliographic record
Abstract
Objective Our goal was to develop and validate a neurophysiological-centric baseline model that can be appreciated by the broader neuro community and practically utilized by the sports community. Background As concussions, a mild traumatic brain injury, and other traumatic brain injuries gain notoriety amongst public awareness, there has been a rise in available computer-based concussion baseline assessments. Furthermore, states, sporting agencies, and schools are mandating implementation of concussion baselines. However, validation and standardization of these current baseline neurological tests have been remiss and are often not utilized by medical or neurological practitioners, making their utility suspect. Design/Methods We applied our neurocognitive baseline program to college football freshmen and high school aged ice hockey players. The list of baseline assessments is: eye-hand coordination reaction time using the Dynavision D2™ device, stereopsis measurements, phoria, oculomotor performance, electroretinography and visual-evoked potential, binocularity, optical coherence tomography, peripheral vision assessments, and balance. Results The results suggest that this baseline program can be performed as a battery appropriate for a pre-participation examination prior to sport participation. The data derived from said baseline can be interpreted by sport, age and gender specific. These demographics can also be examined for developing normative data and useful for identifying subjects outside this normal. Conclusions It is felt that the current state of concussion baselines for athletic organizations are inadequate. We chose to identify a series of baseline tests that are more clinically-relevant and easy to perform as evidenced from the pre-season baseline used by the University of Cincinnati athletes and non-collegiate athletes. These baselines are used as part of the standard pre-participation examination, further providing valuable insight into the development of sports vision training performance enhancement programs and extensively relied upon as a pre-season concussion baseline. We feel this model has enhanced clinical utility compared to the current wide spread computer-based neuropsychological 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".