Can injury in major junior hockey players be predicted by a pre-season functional movement screen - a prospective cohort study.
Bibliographic record
Abstract
BACKGROUND: The Functional Movement Screen (FMS) is a tool that is commonly used to predict the occurrence of injury. Previous studies have shown that a score of 14 or less (with a maximum possible score of 21) successfully predicted future injury occurrence in athletes. No studies have looked at the use of the FMS to predict injuries in hockey players. OBJECTIVE: To see if injury in major junior hockey players can be predicted by a preseason FMS. METHODS: A convenience sample of 20 hockey players was scored on the FMS prior to the start of the hockey season. Injuries and number of man-games lost for each injury were documented over the course of the season. RESULTS: The mean FMS score was 14.7+/-2.58. Those with an FMS score of ≤14 were not more likely to sustain an injury as determined by the Fisher's exact test (one-tailed, P = 0.32). CONCLUSION: This study did not support the notion that lower FMS scores predict injury in major junior hockey players.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".