Prolonged restricted training, fixture congestion and player rotation: What the COVID-19 pandemic taught us about injury risk in professional collision sport
Bibliographic record
Abstract
OBJECTIVES: The COVID19-induced suspension of the 2019-20 professional England rugby union season resulted in players being exposed to an extended restricted training period, coupled with a congested match schedule once competition resumed. We assessed the impact of these changes on match and training injuries in the final 20-weeks of the season following competition resumption. DESIGN: Epidemiological study. METHODS: The 2019-20 season was compared to the previous three seasons (2016-19). RESULTS: There was no significant difference in the mean incidence, severity and burden of training and match injuries in 2019-20 compared to 2016-19 period mean. The 2019-20 post-suspension mean match injury rate [77/1000 h (95%CIs [confidence intervals]: 67-89)] was comparable to the 2019-20 pre-suspension [93/1000 h (95%CIs: 85-101)] and significantly lower than the 2016-19 equivalent post-suspension period [97/1000 h (95CIs: 90-104) IRR [incidence rate ratio] 0.8 p=0.002]. In the 2019-20 season, there was a significantly higher rate of training injury post-suspension in comparison to pre-suspension [3.8/1000 h (95CIs: 3.3-4.4) vs 2.7/1000 h (95% CIs: 2.5-3.1) IRR 1.4 p=0.005]. There was no significant difference in the overall incidence, severity or burden of injuries sustained in fixtures with shorter (<6 days) turnarounds but there was a significantly higher burden of soft tissue injuries. CONCLUSIONS: This is the first study to assess the effect of restricted training on injury risk in collision sports. Players were at an increased risk of training injury when returning from the suspension, but 10-weeks of preparatory training meant the incidence of match injury was not higher when competition resumed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".