Rugby players and whiplash where are all the cases?
Bibliographic record
Abstract
Background Despite efforts to provide a comprehensive diagnosis of whiplash (eg, The Quebec Classification system, 1995; Soderlund & Dennison) it is an ill defined and poorly understood injury. Its occurrence seems to be dependent on a number of contextual variables. Objective The primary objective of this study was to identify the number of cases of whiplash reported in one complete season by professional rugby players. Design A longitudinal design was adopted. The assessment of whiplash was carried out independently of the assessment of the number of incidents that could potentially lead to WAD. The two assessments were blinded to each other. Setting The study was undertaken within a professional rugby club in the north of England. Participants The full first team squad (n=32) were included in the study. Assessment of risk The main independent variables was the number of potential whiplash inducing incidents over the entire season. Main outcome measurements The main outcome measure was the number of whiplash related injuries reported. Results There were six cases of whiplash reported during the entire season. Five followed an on field assessment during the game and the players were deemed fit to continue with play. They needed treatment post injury in readiness for the next game, however in all five cases the injuries were not sufficiently serious to rule the player concerned out of contention for selection and all have missed no matches as a result of their injury. The minimum number of collisions that would have been expected to produce whiplash was found to be three per game. Conclusion It is clear that the number of reported/diagnosed whiplash injuries in this sample is much lower than would be expected if you were to use data from the non-sporting domain. Further investigation into why this discrepancy happens is underway, using physiological, psychological and biomechanical markers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".