Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union
Bibliographic record
Abstract
Wide variations in the definitions and methodologies used for studies of injuries in rugby union have created inconsistencies in reported data and made interstudy comparisons of results difficult. The International Rugby Board established a Rugby Injury Consensus Group (RICG) to reach an agreement on the appropriate definitions and methodologies to standardise the recording of injuries and reporting of studies in rugby union. The RICG reviewed the consensus definitions and methodologies previously published for football (soccer) at a meeting in Dublin in order to assess their suitability for and application to rugby union. Following this meeting, iterative draft statements were prepared and circulated to members of the RICG for comment; a follow-up meeting was arranged in Dublin, at which time all definitions and procedures were finalised. At this stage, all authors confirmed their agreement with the consensus statement. The agreed document was presented to and approved by the International Rugby Board Council. Agreement was reached on definitions for injury, recurrent injury, non-fatal catastrophic injury, and training and match exposures, together with criteria for classifying injuries in terms of severity, location, type, diagnosis and causation. The definitions and methodology presented in this consensus statement for rugby union are similar to those proposed for football. Adoption of the proposals presented in this consensus statement should ensure that more consistent and comparable results will be obtained from studies of injuries within rugby union.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.482 | 0.493 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.017 | 0.011 |
| Research integrity | 0.017 | 0.033 |
| Insufficient payload (model declined to judge) | 0.020 | 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; 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".