Training load and structure-specific load: applications for sport injury causality and data analyses
Bibliographic record
Abstract
### Definitions #### Training load Training load represents step count, throws, distance run and/or time spent practising sport. This can be used to calculate a change in training load over time (eg, acute:chronic workload ratio or week-to-week changes), which has been used as a time-varying exposure to sports injury recently. #### Structure-specific cumulative load Can be viewed as the sum of step-specific or throw-specific loads that a certain musculoskeletal structure is exposed to during a training session. Estimation of the structure-specific cumulative load per training session involves stepwise or throw-wise quantification of the load distribution and the load magnitude. #### Structure-specific load capacity Can be defined as a certain structure’s ability to withstand structure-specific cumulative load. How should I schedule my training? How much is too much? Coaches and sports medicine clinicians commonly face such questions when considering training and injury risk. These are highly relevant inquiries, as training load is a necessary cause of sports injury.1 2 To provide answers, our analytical approaches should align with causal frameworks. Changes in training load (eg, acute:chronic workload ratio) has been used as an interesting exposure to injury lately3–5 and promoted as proximal in the causal chain to sports injury.2 6 However, the aetiology behind sports injury development is multifactorial.1 Therefore, more variables (eg, body mass, alignment, diet, strength) than training load are necessary to robustly identify ‘how much is too much’.7 Accordingly, the purpose of this editorial is to describe the differences among the concepts ‘training-load’, ‘structure-specific load’ and ‘load capacity’, including the varied exposures that define them. Sports injury prevention scientists should carefully consider how best to phrase their research questions in aetiological …
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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 | 0.170 | 0.499 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".