Psychophysiological and neuromotor indicators of brain concussion
Bibliographic record
Abstract
Objective Post-concussion symptoms disappear from days up to 3 months in adults after a 1st concussion. 15% of adults have persistent sequelae. The athletes have a higher probability of experiencing a second concussion and to suffer from both prolonged due to the cumulative effect of these neurological injuries. Lower cognitive and physical skills of the athlete are more documented after concussion. Specifically, the impacts of concussion on neuromotor and neurophysiological functions are investigated. Design Case studies. Settings Rehabilitation in public and private practices. Patients Three athletes who sustained a concussion with persistent symptoms after several months to one year. Interventions Measures of frequency and heart rate variability at night; percentage of cardiac reserve on daily tasks of different intensities and a neuromotor assessment were performed. Main Outcome All three athletes show a cardiac reactivity to a task in mismatch with the actual engineering costs of the various tasks performed and, several months to one year post-concussion. They show motor strategies disorders and slower processing of visual information. Conclusions Cardiac monitoring and neuromotor assessment provides additional indicators in the detection of concussion, as a complement to the neurocognitive assessment, and should be included routinely in the management concussion protocols of athletes. These data are also valuable clinically to help reduce post-concussion symptoms. However research is needed to understand their impact on the recovery of the athlete so as to promote a safe return to play. Competing interests Neurosport is a private pratice who offers services to athlete for post-concussion assessment and neurorehabilitation.
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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) | 0.004 | 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".