Reliability of a Repeated High-Intensity Effort Test for Elite Rugby Union Players
Bibliographic record
Abstract
This study aimed to adapt a repeated high-intensity effort (RHIE) test to the rugby union physical demands and assess both sprint time and tackle indices reliability. Following a familiarization session, sixteen elite rugby union players completed two RHIE tests consisting of 12 × 20 m sprint + tackle. Total sprint time and total g-force during tackling, average sprint time and average g-force as well as percentage decrementsprint time and percentage decrementtackle were considered for the analysis. Sprint time indices showed high to very high absolute and relative reliability (intraclass coefficient correlation (ICC) = 0.95, Standard Error Measurement (SEM) = 1.30%; ICC = 0.95, SEM = 1.44%; ICC = 0.73, SEM = 23.0%, for total sprint time, average sprint time and percentage decrementsprint time, respectively). Tackle indices showed moderate to high reliability (ICC = 0.54, SEM = 16.5%; ICC = 0.61, SEM = 15.6%; ICC = 0.71, SEM = 12.3%, for total g-force, average g-force and percentage decrementtackle, respectively). The RHIE test provides reliable measures of sprint time and tackle indices. Tackle indices should be used as a validation criterion of the test, whereas total time should be considered as the test final result.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".