AOASM Position Statement on Esports, Active Video Gaming, and the Role of the Sports Medicine Physician.
Bibliographic record
Abstract
ABSTRACT: Electronic sports, or esports, has a global audience of over 300 million fans and is increasing in popularity, resulting in projected revenue of over $1 billion by the end of this past year. The global pandemic of 2020 had little to no effect on these increasing numbers because athletes have been able to continue to engage in sports because of its electronic nature and fans have been able to follow them virtually. Esports has been recognized as an organized sport by the International Olympic Committee, the US National Collegiate Athletic Association, and several secondary school athletic associations within the United States. In addition, professional teams have been established in several major cities within the United States, Canada, Europe, and Australia. With the growth of esports, the necessity of incorporating esports medicine into the practice of sports medicine physicians has become paramount. Esports can be played on a monitor or screen and played using physical activity in what has become known as active video gaming. Within both of these platforms, there have emerged certain conditions unique to esports. There are also certain conditions seen in other sports applicable to esports athletes. This document will review the evaluation of the esports athlete, introduce conditions unique to these athletes and review common conditions seen in esports, discuss diagnostics used in the evaluation of esports athletes, introduce treatment options for conditions unique to esports and review those for commonly seen injuries in esports, discuss prevention of injuries in esports, and introduce a framework for the future development of esports medicine that can be introduced into the daily practice of the sports medicine physician.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.025 | 0.017 |
| Insufficient payload (model declined to judge) | 0.050 | 0.036 |
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".