056 The effectiveness of neuromuscular training warm-up programme to reduce knee and ankle injuries in youth basketball: a historical cohort study
Bibliographic record
Abstract
Background Studies evaluating the effectiveness of neuromuscular training (NMT) warm-up programmes in reducing knee and ankle injuries in youth basketball are sparse and specifically, the effect of NMT warm-up programmes on patellar and Achilles tendinopathy is unknown. Objective To evaluate the effectiveness of NMT warm-up programme in reducing the risk of knee and ankle injuries, including patellar and Achilles tendinopathy in youth basketball. Design A two-season historical cohort comparison of players exposed (season 2) and unexposed (season 1) to an NMT intervention. Setting Youth basketball teams (Alberta, Canada). Participants Ninety-four teams, comprising 825 male and female players (age range: 11–18 years; season 1, n=518; season 2, n=307). Interventions A coach-delivered 10-minute SHRed Basketball Injuries NMT warm-up programme, administered in season 2, comprised 13 exercises including aerobic, agility, strength and balance components. The control teams used their standard of practice warm-up in season 1. Main Outcome Measurements All-complaint knee and ankle injuries, including patellar and Achilles tendinopathy were recorded weekly throughout two basketball seasons using validated injury surveillance methods. Poisson regression (with offset using exposure hours and adjusted for team cluster, sex, two-season participation) was used to estimate incidence rate ratios (IRRs; 98.8%CIs Bonferroni) for all-complaint injuries between seasons. Logistic regression (adjusted for team cluster, sex, exposure hours, two-season participation), was used to estimate odds ratios (ORs; 98.8%CIs) for players reporting at least one tendinopathy. Results The NMT warm-up programme was protective for knee [IRR=0.51 (98.8%CI: 0.35–0.75)] and ankle injuries [IRR=0.68 (98.8%CI: 0.52–0.91)] but not for patellar [OR=0.88 (98.8%CI: 0.44–1.73)] and Achilles tendinopathy [OR=0.63 (98.8%CI: 0.18–2.18)] specifically. Conclusions The SHRed Basketball Injuries NMT warm-up programme is effective in reducing all-complaint knee and ankle injury rates but ineffective for mitigating patellar and Achilles tendinopathy risk in youth basketball players. Further research evaluating load modification may be a target for prevention of tendinopathies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".