The effects of training on fatigue and twitch potentiationin human skeletal muscle
Bibliographic record
Abstract
Twitch postactivation potentiation (PAP) in skeletal muscle is a well recognized and accepted phenomenon. However, the mechanisms responsible for potentiation are not understood in detail, and the possible role of potentiation in normal human movement has remained unclear. It is known that potentiation is increased in fatigued compared to rested muscle. We hypothesized that if fatigue and potentiation were directly linked, a training program should increase PAP and reduce fatigue in parallel. Six subjects underwent a muscle stimulation protocol in which twitch contractions were elicited in the knee extensor muscles before and after a 10‐s maximal voluntary contraction (MVC) to detect the degree of PAP. This was done before and after subjects underwent a protocol designated to induce low‐frequency fatigue (knee extensions at 180 · s −1 , organized in three repetitions of 60‐s bouts, separated by 3 min). This whole protocol was done before and after a 4‐week period of isokinetic training, consisting of two sets (5‐min interval) of 10 single MVCs (10‐s intervals), at 90° · s −1 . In non‐fatigued muscles, PAP was greater after training (51.2 ± 4.8%) than before training (44.4 ± 2.4%). In fatigued muscles, PAP was similar before and after training (59.9 ± 2.8% and 60.2 ± 2.6%, respectively). Low‐frequency fatigue was observed before training, as twitch force decreased to 66.8 ± 3.1% of the pre‐fatigue value. After the training period, low‐frequency fatigue was attenuated, as force decreased only to 81.8 ± 2.6% of the pre‐fatigue value. Therefore, it appears that training decreases low‐frequency fatigue and increases PAP. Therefore, the hypothesis that potentiation is partially linked to fatigue in voluntary contracting human skeletal muscles was confirmed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".