Predictive value of strength loss as an indicator of muscle damage across multiple drop jumps
Bibliographic record
Abstract
The aim of the present study was to compare the time-course of indirect symptoms of exercise-induced muscle damage after 50 and 100 drop jumps. A high-force, low intensity exercise protocol was used to avoid discrepancies regarding metabolic fatigue immediately after exercise. Healthy untrained men performed 50 ("50 group", n = 13) or 100 ("100 group", n = 13) intermittent (30-s interval between each jump) drop jumps, respectively, from the height of 0.5 m with a counter-movement to a 90° knee flexion angle and immediate maximal rebound. Voluntary and electrically evoked knee extensor strength was assessed using an isokinetic dynamometer immediately before and at 2 min after exercise, as well as 3, 7, and 14 days after exercise. Creatine kinase (CK) activity and muscle soreness within 7 days after exercise were also determined. The results showed that the decrease in voluntary isometric and isokinetic torque as well as 100 Hz stimulation torque at the end of the 50 and 100 drop jumps was very similar, while substantial differences were found in low-frequency fatigue, shift in optimal knee joint angle, muscle soreness, and CK activity. In addition, there was slower muscle strength recovery after the 100 drop jumps. It is concluded that the predictive value of strength loss immediately after exercise as an indicator of muscle damage decreases as the jump number increases. Still, stimuli must be large enough for muscle torque to reach the reduction plateau. Therefore, magnitude of exercise becomes a major factor in accuracy of muscle damage predictions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".