Role of Neuropsychologists in the Evaluation and Management of Sport-related Concussion: An Inter-Organization Position Statement
Bibliographic record
Abstract
Over the past 20 years, clinical neuropsychologists have been at the forefront of both scientific and clinical initiatives aimed at developing evidence-based approaches to the evaluation and management of sport-related concussion. These efforts have directly impacted current policy on strategies for injury assessment and return-to-play by athletes after concussion. Many states are considering legislation requiring (a) education of athletes, parents, coaches, and school/organization officials on the recognition, evaluation, and management of sport-related concussions; (b) removal from play of any youth athlete that is suspected of having sustained a concussion; and (c) not allowing the student to return to participation until the student is evaluated and cleared for return to participation in writing by an appropriate healthcare professional. It is the official position of the American Academy of Clinical Neuropsychology (AACN), American Board of Neuropsychology (ABN), Division 40 (Neuropsychology) of the American Psychological Association (APA), and the National Academy of Neuropsychology (NAN) that neuropsychologists should be included among the licensed health care professionals authorized to evaluate, clinically manage, and provide return to play clearance for athletes who sustain a sport-related concussion.
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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 | 0.081 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.040 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 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".