054 Supervised implementation of a neuromuscular training warm-up programme to improve adherence and reduce injuries in youth basketball: a cluster randomised trial
Bibliographic record
Abstract
Background The efficacy of neuromuscular training (NMT) programmes has been extensively documented; however, little is known about the best strategies to translate them into practice. Objective To compare the effects of a supervised vs. unsupervised implementation of an NMT warm-up programme on team adherence and injury risk in youth basketball players. Design A pragmatic cluster randomised controlled trial. Setting High school basketball teams in Alberta, Canada. Participants 31 teams (18 female teams) comprising 307 players (age range: 14–18 years). Interventions A structured pre-season coach workshop with (intervention) or without (control) weekly research staff supervision of a 10-minute NMT warm-up programme, comprising 13 exercises was administered. Teams were asked to perform the NMT warm-up programme before every practice and game through the 2017/2018 basketball season. Main Outcome Measurements Team adherence, evaluated as cumulative utilisation (proportion of total NMT sessions possible), utilisation fidelity (average number of exercises completed per NMT session) and utilisation frequency (average number of NMT sessions completed per week), was tracked daily by team designates. All-complaint injuries were collected weekly. Wilcoxon sign rank tests or Poisson regressions were used for the analyses, with Bonferroni corrections. Results No significant differences were found in the median (range) cumulative utilisation [80% (32%–100%) vs. 75% (16%–100%)], utilisation fidelity [12.1 (5.5–13.0) vs. 11.4 (5.1–13.0)] and utilisation frequency [2.2 (0.9–4.1) vs. 2.2 (0.5–4.7)] between intervention and control groups, respectively (all p>0.017). Injury incidence rates, adjusted for cluster by team, sex and age did not differ by groups for all injuries [incidence rate ratios (IRR) = 1.21 (97.5%CI: 0.73–1.99)] and lower extremity injuries [IRR = 1.10 (97.5%CI: 0.73–1.66)]. Conclusions No additional benefits were found in adherence or injury risk reduction following a supervised implementation of an NMT programme in youth basketball. This implementation strategy should not be considered for broad-scale translation of NMT programmes in this context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".