79 (12A) Development of educational resources to support knowledge transfer related to the 6<sup>th</sup> concussion in sport consensus statement
Bibliographic record
Abstract
Purpose The Concussion in Sport Group Education Committee (CISG-EC) is a multidisciplinary group of members spanning several countries. The mission of the CISG-EC includes 1) facilitating access to and providing evidence-based educational resources for the CISG membership and 2) developing and coordinating outreach and information for public access. To fulfill this mission, the CISG-EC has developed tangible educational and outreach materials.Methods The committee voted on several types of materials for the general public and healthcare professionals, including webinars, infographics and brief videos to be used on social media platforms. CISG-EC members voted on topics for webinars and infographics. Webinars including leading content experts were reviewed by Committee Chairs and published to the CISG site for its membership. Infographics were created and edited by CISG-EC members through virtual meetings and email. All print materials produced were approved by the CISG Executive Committee. Viral videos will be created for medical professionals and the general public, edited, and reviewed by the CISG Executive Committee.Results To date, the CISG-EC has produced three webinars and four infographics that will be available on the CISG website (https://www.concussioninsportgroup.com). The webinars include 1) overview of CISG, 2) SCAT6/SCOAT6, and 3) Child SCAT6/SCOAT6. The infographics describe the 1) Concussion Recognition Tool, 2) Return-to-Sport, 3) Return-to-Learn, and 4) a general public infographic for Return-to-Sport.Conclusion The CISG-EC has successfully produced educational materials for the CISG membership and initiated outreach to the general public. Additional educational materials including viral videos will be developed in the future.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.016 |
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".