Lower-Limb Power cannot be Estimated Accurately from Vertical Jump Tests
Bibliographic record
Abstract
The countermovement jump test is often adopted to monitor lower-limb power of an individual. Despite several studies on the validity of this test, there is still a need to determine the minimal difference needed to be confident that a difference in power between two individuals is present or that a true change in the performance of an individual has occurred. In this study, power was measured from ground reaction forces and compared to that obtained from predictive equations for two groups of subjects (67 trained and 20 highly trained individuals). The height of each jump was determined with kinematic techniques. The main outcome is a large discrepancy between power calculated from ground reaction forces and that calculated from predictive equations. For the trained group, the R-square value between power and predicted power was 0.53 and the minimal difference to consider that two individuals were different was 821.7 W. For the highly trained individuals, a much larger R-square value was obtained (0.94). Despite this, the minimal difference to consider that two individuals were different was still large (689.3 W). The large minimal differences obtained raise serious concerns about using countermovement jumps for appraisal and monitoring of lower-limb power of an individual.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".