Eccentric resistance training of the knee extensor muscle: Training programs and neuromuscular adaptations
Bibliographic record
Abstract
PURPOSE: This review is aimed at describing the methods used in knee extensor eccentric resistance training in healthy subjects and at evaluating the adaptations in strength, activation and structure of this muscle group. METHODS: Seventy-five studies were carefully analyzed and 30 are considered in this review. RESULTS: Training programs comprised of 1-4 sessions per week for a period ranging from four to 20 weeks with isokinetic dynamometers or conventional strength training machines were considered. Isokinetic eccentric training programs included 1-6 sets of 6-12 repetitions, while isotonic eccentric training programs consisted of 3-7 sets of 5-10 repetitions. Eccentric strength gains per training session (0.45-3.42%) were typically found to be greater compared to isometric (0.08-1.30%) and concentric (0.23-1.44%) strength gains. Quadriceps activation was improved in tests performed eccentrically and isometrically, but there is poor evidence of increased concentric activation and reduced co-activation of antagonistic muscles. Regarding muscle structure, significant hypertrophic responses have been demonstrated through increases in anatomical/physiological cross-sectional area, muscle thickness and fiber diameter. Most studies measuring muscle architectural changes reported increased fascicle lengths without changes in pennation angle. Adaptations in fiber type distribution were inconsistent. CONCLUSIONS: Strength gains following knee extensor eccentric training are caused by neural and structural adaptations, and may contribute to physical fitness in healthy populations and health improvement in patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".