The intention-to-treat effect of changes in planned participation on injury risk in adolescent ice hockey players: A target trial emulation
Bibliographic record
Abstract
OBJECTIVES: Target trial emulation is a framework for conducting causal inference using observational data. We employ this framework to estimate the effect of changing planned participation duration, measured using the acute:chronic workload ratio (ACWR), on injury risk among adolescent ice hockey players without recent injuries. DESIGN: Prospective cohort study designed to emulate a hypothetical randomized trial. METHODS: We used data from a 5-year cohort study (2013-2018) of ice hockey players aged 13-17 years in Alberta and British Columbia. We estimated injury risks associated with different planned changes in hockey participation duration (e.g. half [ACWR = 0.5], no change [ACWR = 1], two-fold [ACWR = 2], three-fold [ACWR = 3], and five-fold [ACWR = 5]) relative to participation in the previous 4 weeks. Outcomes were modeled using generalized additive models. We conducted secondary analyses restricted to concussions, and stratified by league bodychecking status. RESULTS: There were 2633 eligible participants, contributing 115,821 player-trials. Injury risk was 1.9 % (95 % CI: 1.7 %-2.3 %) for no change in participation (ACWR = 1). Injury risk ratios (RRs) were 0.43 at ACWR = 0.5 (95 % CI: 0.31-0.54), 1.62 (95 % CI: 1.33-1.98) at ACWR = 2, 1.91 at ACWR = 3 (95 % CI: 1.52-2.48) and 2.35 at ACWR = 5 (95 % CI: 1.68-3.26). Patterns were similar by league bodychecking status. Concussion RRs were stable between ACWR = 1 and 1.5, but RRs were greater than for any injury past ACWR = 2. CONCLUSIONS: Within the assumptions of this target trial emulation, injury risk increases consistently (no sweet spots) for increases in planned changes in participation duration relative to the previous 4 weeks among adolescent ice hockey players without recent injuries. Injuries in injury risk are less than expected for the increased exposure time at risk, suggesting beneficial effects of increasing participation that partially counteract the increased exposure time.
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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 | 0.241 | 0.232 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".