Injury in Starting and Replacement Players from Five Professional Men’s Rugby Unions
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to compare the incidence, severity, and burden of injury in starting and replacement players from professional men's teams of five rugby unions. METHODS: Match injuries of greater than 24 h time-loss (including data on the severity, match quarter, event, body region) and player minutes of match exposure data were collated for all starting and replacement players in the men's English Premiership, Welsh Pro14 (both 2016/17-2018/19 seasons), and Australian, New Zealand, and South African Super Rugby (all 2016-2018 seasons) teams. Injury incidences and mean injury burden (incidence × days missed) were calculated, and rate ratios (RRs) (95% confidence intervals [CIs]) were used to compare injury incidence and burden between starting (reference group) and replacement players. RESULTS: Overall injury incidence was not different between starters and replacements for all injuries (RR = 0.98, 95% CI 0.88-1.10), nor for concussions (RR = 0.85; 95% CI 0.66-1.11). Mean injury burden was higher for replacement players (RR = 1.31, 95% CI 1.17-1.46). Replacement injury incidence was lower than the starters in the third (RR = 0.68, 95% CI 0.51-0.92) and fourth (RR = 0.78, 95% CI 0.67-0.92) match quarters. Injury incidence was not different between starters and replacements for any match event or body region, but compared with starters, replacements' injury burden was higher in lower limbs (RR = 1.24, 95% CI 1.05-1.46) and in the tackled player (RR = 1.30, 95% CI 1.01-1.66). CONCLUSION: This study demonstrated a lower injury incidence in replacement players compared with starters in the second half of matches, with a higher injury burden for replacement players due to higher mean injury severity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".