Consensus Statement on Sports-Related Concussions in Youth Sports Using a Modified Delphi Approach
Bibliographic record
Abstract
Importance: Given the importance of sports-related concussions among youth athletes, the rapid progress of research on this topic over the last decade, and the need to provide further guidance to youth athletes, their families, medical professionals, and athletic personnel and organizations, a panel of experts undertook a modified Delphi consensus process to summarize the current literature and provide recommendations regarding the prevention, assessment, and management of sports-related concussions for young athletes. Methods: A consensus panel of 11 experts was created to represent a broad spectrum of expertise in youth sports and concussions. The specific questions to be addressed were developed through an iterative process consisting of 3 rounds, and a review of the literature was conducted to identify research studies related to each question. The consensus panel used a modified Delphi process to reach consensus on the conclusions and recommendations for each question. Results and Conclusions: In 3 Delphi consensus rounds, 7 questions were addressed by the consensus panel of 11 experts, and 26 recommendations for the prevention, assessment, and management of sports-related concussions among young athletes were developed. For many of the questions addressed in this consensus statement, limitations existed in the quantity and quality of the evidence available to develop specific recommendations for youth sports stakeholders.
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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 | 0.252 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".