Impaired Jump Landing After Exercise in Recreational and in High-Performance Athletes
Bibliographic record
Abstract
The risk of sustaining injuries increases with fatigue. The aim of this study was to analyze the influence of fatigue on dynamic postural control in jump landing and stabilization (ST) in athletes of different levels. In all, 18 high-performance ball sports athletes and 24 recreationally active subjects performed a jump test (JT) before and at 1, 5, 10, 15, and 20 minutes after a 30-minute treadmill run at the individual anaerobic threshold. An overhead ball switch hit during a forward jump triggered indicator lamps on either side of a force plate. After landing on the plate, ST on 1 leg (no light cue) or a second jump sideways (toward a light cue) was required. The ST force integral index was calculated for the ST trials. Dynamic postural control was significantly impaired in jump landing and ST in the first minute after the run: mean difference ± SD: 0.25 ± 0.48 m·s-1 (95% confidence interval: 0.10-0.40 m·s-1, p = 0.043; analysis of variance). No significant group differences were found. Under fatigued conditions, dynamic postural control in jump landing was impaired in an unexpected ST task. Not only recreational but also high-performance athletes were affected. Ball sports athletes could add a training exercise to their workout, which alternates between periods of high effort and neuromuscular training. Resistance to fatigue effects should be checked on a regular basis using JTs.
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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 | 0.001 |
| 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".