The Relationship between Components of the Dynamic Strength Index and the Slope of the Force-Velocity Profile in the Loaded Countermovement Jump in Resistance-Trained Males and Females
Bibliographic record
Abstract
The purpose of this study was to evaluate the relationship between the dynamic strength index (DSI) and the lower-body Force-velocity (F-v) profile. Eighty-six (n = 58 females) resistance-trained individuals were recruited to perform both the DSI and F-v profile testing protocols to evaluate this relationship, as well as relationships between the components that comprise each test. Spearman correlations were calculated between DSI, F-v profile slope, countermovement jump (CMJ) peak force (PF), isometric mid-thigh pull (IMTP) PF, and CMJ peak velocity (PV) across a series of loading conditions from an unloaded CMJ to an additional 100% bodyweight (BW) CMJ condition. No significant correlations (rs = 0.01; p > 0.05) were found between the DSI value and the F-v profile slope. Significant correlations were found between the DSI and CMJ/IMTP PF (rs range = -0.63 to 0.22; p < 0.05) and between CMJ/IMTP PF and measures of CMJ PV (rs range = 0.45 to 0.73; p < 0.05) across the loading conditions. Results suggest that the DSI is not correlated to the F-v profile slope. Two different means of evaluating muscular force in athletes are not correlated; we suggest that athletes require specific evaluations for specific performance characteristics when assessing muscular force.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".