Abnormal Motor Response Associated With Concussive Injuries: Biomechanical Comparison Between Impact Seizures and Loss of Consciousness
Bibliographic record
Abstract
CONTEXT: Loss of consciousness (LOC) and impact seizures associated with concussion represent different clinical presentations of concussion; however, they are often investigated and treated similarly. The biomechanical parameters differentiating these 2 distinct signs of injury are poorly described. OBJECTIVE: To differentiate between cases of concussions with LOC and those with impact seizures by comparing the impact velocity, peak linear and peak rotational acceleration, as well as brain tissue deformation in the cerebral cortex, white matter, brainstem, cerebellum, thalamus, and corpus callosum. DESIGN: Descriptive laboratory study. PATIENTS OR OTHER PARTICIPANTS: Elite American football players who sustained an LOC (n = 20) or impact seizures (n = 21). MAIN OUTCOME MEASURE(S): Impact velocity, peak linear and peak rotational acceleration, maximum principal strain, cumulative strain damage measure at 10%, and strain rate (SR). RESULTS: The SR in the cerebral white matter was greater in the LOC group than in the impact-seizure group. Similar trends were observed for SRs in the cerebral cortex, brainstem, and corpus callosum. No differences were present between groups for the other variables in this study. CONCLUSIONS: A lower SR in certain brain regions helps to explain why motor function is preserved and can be observed in patients with impact seizures versus LOC from concussive injuries.
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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 | 0.000 |
| 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) | 0.002 | 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".