Preseason Functional Movement Screen Predicts Risk of Time-Loss Injury in Experienced Male Rugby Union Athletes
Bibliographic record
Abstract
The purpose of this study was to determine the relationship between Functional Movement Screen (FMS) score and the risk of time-loss injury in experienced male rugby union athletes. A secondary purpose was to determine the relationship between FMS-determined asymmetries and the risk of time-loss injury in these athletes. Functional Movement Screen scores were collected from male rugby union athletes (n = 73) during preseason and half-way through one 8-month season. Time-loss injury data were collected throughout the full season. A receiver-operator characteristic curve was created for each half of the season to identify FMS composite and asymmetry cut-off scores associated with increased likelihood of injury and determined odds ratios, sensitivity, and specificity in evaluating FMS as a predictor of injury risk. Odds ratio analyses revealed that when compared with those scoring >14, athletes with an FMS ≤14 were 10.42 times more likely (95% confidence interval [CI]: 1.28-84.75, p = 0.007) to have sustained injury in the first half of the season and 4.97 times (95% CI: 1.02-24.19, p = 0.029) more likely in the second half of the season. The presence of asymmetries was not associated with increased likelihood of injury. Experienced male rugby union athletes with FMS composite scores ≤14 are significantly more likely to sustain time-loss injury in a competitive season than those scoring >14. The quality of fundamental movement, as assessed by the FMS, is predictive of time-loss injury risk in experienced rugby union athletes and should be considered an important preseason assessment tool used by strength and conditioning and medical professionals in this sport with inherently high injury rates.
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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 | 0.001 | 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) | 0.002 | 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".