Comparison of dynamic response and maximum principal strain of diagnosed concussion in professional men’s rugby league
Bibliographic record
Abstract
Rugby league has been identified as a contact sport with a high incidence of concussion. Research has been conducted to describe incidence and mechanisms of concussion in rugby league, however the risks associated with injury events (shoulder, hip, head to head) are unknown. The purpose of this study was to describe the common injury events leading to concussion in the National Rugby League and compare these events through analysis of dynamic response and brain tissue deformation. Twenty-seven impact videos of concussive injuries were physically reconstructed to obtain linear and rotational accelerations of the head. Dynamic response data were input into the University College Dublin Brain Trauma Model (UCDBTM) to calculate maximum principal strain (MPS). Head-to-head events produced a short duration event with an average peak linear and peak rotational acceleration of 205 g and 15,890 rad/s2, respectively, which were significantly greater than the longer duration hip-to-head (24.7 g and 2650 rad/s2) and shoulder-to-head (24.2 g and 3280 rad/s2) impacts. There were no differences in MPS between events. These results suggest that risk of strain to the brain may be produced by short and long duration acceleration events. Thus, both of these accelerations need to be accounted for in the development of improved head and body protection in rugby. In addition, this data demonstrates that these events cause a risk of concussion requiring efforts to limit or modify how energy is transferred to the head.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".