The “SHRed Injuries Basketball” Neuromuscular Training Warm-up Program Reduces Ankle and Knee Injury Rates by 36% in Youth Basketball
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of a neuromuscular training warm-up prevention program, Surveillance in High school and community sport to Reduce (SHRed) Injuries Basketball, for reducing all-complaint ankle and knee injuries in youth basketball players. DESIGN: Quasi-experimental study. METHODS: High school/club basketball teams (male and female players aged 11-18 years) in Calgary, Canada participated in 2016-2017 (control; season 1) and 2017-2018 (intervention; season 2). The control season included a standard-of-practice warm-up. In season 2, a SHRed Injuries Basketball coach workshop was completed by participating team coaches. Teams were randomized by school/club to an unsupervised or a supervised (weekly supervision by study personnel) implementation of the coach-delivered SHRed Injuries Basketball program. The 10-minute SHRed Injuries Basketball program included 13 exercises (ie, aerobic, agility, strength, balance). All-complaint ankle and knee injuries were collected weekly using validated injury surveillance. Multilevel, multivariable Poisson regression analyses (considering important covariates, clustering by team and individual, and offset by exposure hours) estimated incidence rate ratios (IRRs) by intervention group (season 1 versus season 2) and secondarily considered the control versus completion of the SHRed Injuries Basketball program, unsupervised and supervised. RESULTS: Sixty-three teams (n = 502 players) participated in season 1 and 31 teams (n = 307 players: 143 unsupervised, 164 supervised) participated in season 2. The SHRed Injuries Basketball program was protective against all-complaint knee and ankle injuries (IRR = 0.64; 95% confidence interval [CI]: 0.51, 0.79). Unsupervised (IRR = 0.62; 95% CI: 0.47, 0.83) and supervised (IRR = 0.64; 95% CI: 0.49, 0.85) implementations of the SHRed Injuries Basketball program had similar protective effects. CONCLUSION: .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".