Why do workload spikes cause injuries, and which athletes are at higher risk? Mediators and moderators in workload–injury investigations
Bibliographic record
Abstract
Spikes in training and competition workloads, especially in undertrained athletes, increase injury risk.1 However, just as attributing athletic injuries to single risk factors is an oversimplification of the injury process,2 3 interpreting this workload-injury relationship should not be done in isolation. Instead, we must further unpack how (ie, through which mechanisms) workload spikes might result in injury, and what characteristics make athletes more robust or more susceptible to injury at any given workload. In other words, which factors mediate the workload-injury relationship, and which moderate the relationship. Like dominoes being knocked over, mediators can be viewed as the intermediary steps that explain the association between an observed variable and an outcome.4 In this context, mediating variables help to explain ‘why changes in workloads might cause injuries?’ For example, it is known that rugby league players exposed to spikes in running workloads, indicated by a high acute:chronic workload ratio, are at an increased risk for non-contact injuries.5 One potential explanation is that neuromuscular fatigue mediates this relationship, such that increased …
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".