Assessment and Monitoring of Ballistic and Maximal Upper-Body Strength Qualities in Athletes
Bibliographic record
Abstract
PURPOSE: To evaluate whether the dynamic strength index (DSI: ballistic peak force/isometric peak force) could be effectively used to guide specific training interventions and detect training-induced changes in maximal and ballistic strength. METHODS: Twenty-four elite male athletes were assessed in the isometric bench press and a 45% 1-repetition-maximum (1RM) ballistic bench throw using a force plate and linear position transducer. The DSI was calculated using the peak force values obtained during the ballistic bench throw and isometric bench press. Athletes were then allocated into 2 groups as matched pairs based on their DSI and strength in the 1RM bench press. Over the 5 wk of training, athletes performed either high-load (80-100% 1RM) bench press or moderate-load (40-55% 1RM) ballistic bench throws. RESULTS: The DSI was sensitive to disparate training methods, with the bench-press group increasing isometric bench-press peak force (P=.035, 91% likely), and the ballistic-bench-throw group increasing bench-throw peak force to a greater extent (P≤.001, 83% likely). A significant increase (P≤.001, 93% likely) in the DSI was observed for both groups. CONCLUSIONS: The DSI can be used to guide specific training interventions and can detect training-induced changes in isometric bench-press and ballistic bench-throw peak force over periods as short as 5 wk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".