Snow sports-specific extension of the IOC consensus statement: methods for recording and reporting epidemiological data on injury and illness in sports
Bibliographic record
Abstract
The International Olympic Committee's (IOC) consensus statement on 'methods for recording and reporting of epidemiological data on injury and illness in sport' recommended standardising methods to advance data collection and reporting consistency. However, additional aspects need to be considered when these methods are applied to specific sports settings. Therefore, we have developed a snow sports-specific extension of the IOC statement to promote the harmonisation of injury and illness registration methods among athletes of all levels and categories in the different disciplines governed by the International Ski and Snowboard Federation (FIS), which is also applicable to other related snow sports such as biathlon, ski mountaineering, and to some extent, para snow sports. The panel was selected with the aim of representing as many different areas of expertise/backgrounds, perspectives and diversity as possible, and all members were assigned to thematic subgroups based on their profiles. After panel formation, all members were provided with an initial draft of this extension, which was used as a basis for discussion of aspects specific to the discipline, application context, level and sex within their snow sports subgroup topic. The outcomes were then aligned with the IOC's existing consensus recommendations and incorporated into a preliminary manuscript draft. The final version of this snow sports-specific extension was developed and approved in two iterative rounds of manuscript revisions by all consensus panel members and a final meeting to clarify open discussion points. This snow sports-specific extension of the IOC statement is intended to guide researchers, international and national sports governing bodies, and other entities recording and reporting epidemiological data in snow sports to help standardise data from different sources for comparison and future research.
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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 | 0.503 | 0.535 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".