Para sport translation of the IOC consensus on recording and reporting of data for injury and illness in sport
Bibliographic record
Abstract
In 2020, the IOC proposed a universal methodology for the recording and reporting of data for injury and illness in sport. Para sport is played by individuals with impairment, and they have a unique set of considerations not captured by these recommendations. Therefore, the aim of this addendum to IOC consensus statement was to guide the Para sport researcher through the complexities and nuances that should be taken into consideration when collecting, registering, reporting and interpreting data regarding Para athlete health. To develop this translation, experts in the field of Para sports medicine and epidemiology conducted a formal consensus development process, which began in March 2020 with the formation of a consensus group that worked over eight phases, incorporating three virtual consensus meetings to finalise the translation. This translation is consistent with the IOC consensus statement, yet provides more detailed Para athlete specific definitions and recommendations on study population, specifically, diagnostic and eligible impairment categorisation and recording of adaptive equipment, and defining and classifying health problems in the context of Para sport. Additionally, recommendations and Para athlete specific examples are described with regards to injury mechanism, mode of onset, injury and illness classification, duration, capturing and reporting exposure and risk. Finally, methods and considerations are provided to cater to the varied needs of athletes with impairment with respect to data collection tools. This harmonisation will allow the science to develop and facilitate a more accurate understanding of injury and illness patterns for tailoring evidence-informed prevention programmes and enabling better planning of medical services for Para sport events.
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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 | 0.239 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".