Acute:chronic training loads in tennis: which metrics should we monitor?
Bibliographic record
Abstract
Recently, Pluim and Drew1 provided tips for managing loads to help reduce injury risk in tennis. A central premise was the importance for assessing the acute:chronic loads—a concept to understand that the rate of change towards high weekly loads is more problematic (ie, increases injury risk) than simply performing high loads. In general, activities performed by athletes can be viewed as stress (ie, external loads like running distances or the number of accelerations/decelerations performed) and strain (ie, internal loads like heart rate, blood lactate). Thus, metrics from several domains are necessary to comprehensively quantify training and competition loads; however, there is a paucity of literature describing these indicators for tennis across the developmental spectrum. This knowledge gap limits the ability to identify which key metrics should be targeted within a systematic monitoring plan and subsequently used to track the acute:chronic loads in tennis, thus enabling the development of strategies to reduce injury risk. To …
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".