Neurometabolic, Electrophysiological, and Imaging Abnormalities
Bibliographic record
Abstract
Sport-related concussions affect millions of athletes every year, but they generally present no anatomic alterations when examined using conventional magnetic resonance imaging or a computed tomography scan. Because the damage occurring after a head injury seems to be more functional than structural, these techniques are unable to detect subtle alterations. The absence of detectable structural alterations using traditional neuroimaging methods, the presence of persistent symptoms in some athletes, and the risk of developing neurodegenerative diseases emphasize the importance of assessing the impacts of a head injury with alternative neuroimaging techniques. Electrophysiological methods, magnetic resonance spectroscopy, and diffusion tensor imaging are useful techniques that are sensitive to the effects of a brain trauma, which provide complementary information to allow a more complete understanding of the multiple pathophysiological processes involved in concussive events. This report summarizes recent data using neurophysiological and neuroimaging techniques to better understand the acute and chronic effects of sport-related concussions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".