Development of the Sports Organization Concussion Risk Assessment Tool (SOCRAT)
Bibliographic record
Abstract
OBJECTIVE: In this paper, we describe the development of a novel tool-the Sports Organization Concussion Risk Assessment Tool (SOCRAT)-to assist sport organizations in assessing the overall risk of concussion at a team level by identifying key risk factors. METHODS: We first conducted a literature review to identify risk factors of concussion using ice hockey as a model. We then developed an algorithm by combining the severity and the probability of occurrence of concussions of the identified risk factors by adapting a risk assessment tool commonly used in engineering applications. RESULTS: The following risk factors for ice hockey were identified: age, history of previous concussions, previous body checking experience, allowance of body checking, type of helmet worn and the game or practice environment. These risk factors were incorporated into the algorithm, resulting in an individual risk priority number (RPN) for each risk factor and an overall RPN that provides an estimate of the risk in the given circumstances. CONCLUSION: The SOCRAT can be used to analyse how different risk factors contribute to the overall risk of concussion. The tool may be tailored to organizations to provide: (1) an RPN for each risk factor and (2) an overall RPN that takes into account all the risk factors. Further work is needed to validate the tool based on real data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".