A preliminary analysis of biomechanics and saccadic responses for concussion
Bibliographic record
Abstract
Background Concussion is an injury that occurs in non-sporting and sporting environments. To determine improved clinical methods for identifying this injury, it is important to develop and understand how the impact event results in quantifiable differences in brain functioning—functioning that has been quantified in the past using saccadic measures. The purpose of this research was to examine the relationships between oculomotor deficits, specifically antisaccade responses, and the biomechanics of impact for a concussion. Methods Participants underwent a diffusion tensor imaging protocol as well as saccadic testing to determine differences in brain functioning in comparison to controls. The injury event was then reconstructed in laboratory using physical and finite element models to determine the biomechanics of the impact and brain tissue strain. Relationships between the biomechanical variables and antisaccade responses were then examined. Results The diffusion tensor imaging analyses found that there was a decrease in radial diffusivity and axial diffusivity found in the cerebral peduncle (p < 0.05) and cingulum hippocampus (p < 0.05), respectively. There was an increase in the axial diffusivity for the corona radiata (p < 0.05). The saccadic testing found an increase in mean latency for the concussed group (p < 0.05). The results indicated no significant relationship between mean latency, duration, amplitude and peak velocity antisaccade measures and the biomechanical variables. This may have been influenced not only by a lack of sensitivity in biomechanical variable to antisaccade responses, but also to these responses being affected by factors other than injury such as attentiveness and wakefulness. Conclusion While the sample of this research was small, this research suggests that to improve the understanding of the relationship between impact biomechanics and concussion, methods that can quantify the damage to brain structures through imaging, such as diffusion tensor imaging, may be more appropriate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".