Sub-Concussive Impact Analysis in Collegiate Football Athletes
Bibliographic record
Abstract
Contact athletes are at a significantly elevated risk of experiencing sub-concussive impacts—those that do not reach the threshold to cause typical concussion symptoms but may still contribute to long-term neurological damage (Johnson et al., 2014). Current literature suggests that repeated exposure to these impacts may be linked to neurodegeneration, potentially resulting in long-term cognitive deficits and neurodegenerative disorders in this group (Stern et al., 2011). Previous research has relied on helmet accelerometers to detect and quantify the magnitude of these impacts by measuring peak linear acceleration and peak rotational velocity(Champagne et al., 2019). However, numerous studies have revealed that helmet-mounted accelerometers often overestimate both the acceleration and the severity of impacts, leading to inaccuracies in data collection (Camarillo et al., 2013, Greybe et al., 2020, O’Connor et al., 2017). To overcome this limitation, we hypothesize that acceleration measurements obtained from mouthguard accelerometers will show significantly lower peak linear and rotational accelerations compared to helmet-mounted accelerometers when measuring impacts over the same duration in Queen's University Football players. Each participating player will be fitted with a custom boil-and-bite mouthguard accelerometer engineered by Prevent Biometrics, ensuring a closer coupling to the skull for enhanced measurement accuracy. The players will wear these mouthguards throughout training camp and the season, with each impact exceeding a 15-g threshold validated through video analysis. We expect that the study’s findings will include lower recorded peak linear velocity, peak rotational velocity and more frequent sub-concussive exposure due to the increased sensitivity of the device. These findings will support the current literature that mouthguard accelerometers offer an accurate representation of the forces experienced by athletes. These findings will contribute to the growing body of knowledge on sub-concussive exposure in football players. The research has the potential to identify high-risk hitting patterns and influence new protocols for concussion management. Ultimately, this could increase player safety and promote healthier athletic development across all levels of competition. References: Camarillo, D. B., Shull, P. B., Mattson, J., Shultz, R., & Garza, D. (2013). An Instrumented Mouthguard for Measuring Linear and Angular Head Impact Kinematics in American Football. Annals of Biomedical Engineering, 41(9), 1939–1949. https://doi.org/10.1007/s10439-013-0801-y Champagne, A. A., Peponoulas, E., Terem, I., Ross, A., Tayebi, M., Chen, Y., Coverdale, N. S., Nielsen, P. M. F., Wang, A., Shim, V., Holdsworth, S. J., & Cook, D. J. (2019). Novel strain analysis informs about injury susceptibility of the corpus callosum to repeated impacts. Brain Communications, 1(1), fcz021. https://doi.org/10.1093/braincomms/fcz021 Greybe, D. G., Jones, C. M., Brown, M. R., & Williams, E. M. P. (2020). Comparison of head impact measurements via an instrumented mouthguard and an anthropometric testing device. Sports Engineering, 23(1), 12. https://doi.org/10.1007/s12283-020-00324-z Johnson, B., Neuberger, T., Gay, M., Hallett, M., & Slobounov, S. (2014). Effects of Subconcussive Head Trauma on the Default Mode Network of the Brain. Journal of Neurotrauma, 31(23), 1907–1913. https://doi.org/10.1089/neu.2014.3415 O’Connor, K. L., Rowson, S., Duma, S. M., & Broglio, S. P. (2017). Head-Impact–Measurement Devices: A Systematic Review. Journal of Athletic Training, 52(3), 206–227. https://doi.org/10.4085/1062-6050.52.2.05 Stern, R. A., Riley, D. O., Daneshvar, D. H., Nowinski, C. J., Cantu, R. C., & McKee, A. C. (2011). Long-term Consequences of Repetitive Brain Trauma: Chronic Traumatic Encephalopathy. PM&R, 3(10S2), S460–S467. https://doi.org/10.1016/j.pmrj.2011.08.008
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".