Neurocognitive Performance: Returning to Competition
Bibliographic record
Abstract
Athletes who suffer from concussions under report their symptoms in order to expedite their return to competition. Athletic trainers and coaches must be aware of what is going on with athletes, even if it means requiring them to refrain from competition. Ninety percent of concussions are minor and can be difficult to diagnosis. There is a lack of guidelines available for physicians and athletic trainers to follow when dealing with concussions. However, healthcare officials are recognizing the importance of concussion management and experts agree that athletes who have concussion symptoms should not return to competition until they are fully resolved. Computerized testing can efficiently and effectively assess and diagnose a concussion. Physicians and athletic trainers can monitor these tests, which saves athletes time and money. Computerized tests such as ImPACT and CRI provide clear results that are easy to read. By reviewing the results physicians and athletic trainers may better diagnose the symptoms which prevent the athletes from being dishonest about their symptoms in order to return to competition before they have recovered.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".