Tackle characteristics associated with concussion in elite men’s rugby union: unpicking the differences between tacklers and ball-carriers
Bibliographic record
Abstract
Objective To identify characteristics of tackling, of being tackled and interactions between tackle characteristics that are associated with concussion. Methods A case-control study in male professional rugby union players in England over five seasons (2016/2017 to 2020/2021) analysed characteristics of tackles that led to a clinically diagnosed concussion (cases), and a control group of tackles that did not result in a concussion. ORs were plotted against the overall frequency of each tackle characteristic. Results 231 tackles resulting in concussions (tackler 178, 77%; ball-carrier 53, 23%), alongside 9963 control tackles, were analysed. For tacklers, ‘head to torso’ ( Lower CI OR Upper CI; 4.0 6.5 10.7 ) had relatively low odds of concussion compared with ‘no head contact’. ‘Head to knee’ had the highest odds of concussion ( 75.3 155.8 322.4 ), but ‘head to hip’ occurred more frequently and had the highest number of concussions per 1000 tackles (3.1/1000 tackles). For ball-carriers, ‘head to head’ contact had the highest odds of concussion ( 56.7 104.3 192.0 ). When ‘tackler body position’ was ‘upright’, the odds of concussion to the tackler were significantly higher when contacting the ball-carrier’s ‘head and neck’ versus their ‘torso’ ( 3.0 23.7 206.7 ). Conclusions Lower tackles reduce the chances of concussion to ball-carriers. The influence of tackle height on concussion to tacklers is more nuanced, but the chances are relatively low when contact is made with the ball-carrier’s torso. These findings support ongoing implementation of strategies to reduce concussion risk by lowering tackle height to target the torso.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".