A Model of Current Best Practice for Managing Concussion in University Athletes: The University of Toronto Approach
Bibliographic record
Abstract
Along with the ever growing awareness among the scientific community and the general public that concussion is a serious health care issue at all levels of sport, with potentially devastating long term health effects, the number of concussion surveillance clinical monitoring programs has significantly increased internationally over the past 10–15 years. An effective concussion program (a “best practice” model) is clinically prudent and evidence-based, one that is an interdisciplinary model involving health professionals who manage, educate, and provide psychosocial support to athletes. The integration of neuropsychological assessment is a component of many present day programs, and therefore, the neuropsychologist is an integral member of the concussion management team. The University of Toronto Concussion Program, operational since 1999, integrates best practices and current evidence into a working model of concussion management for university athletes. The model uses an interdisciplinary approach to monitor and assess athletes with concussions, as well as to educate its athletes, coaches, and administrators. A research component is also integral to the program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".