Potentiation and Electrical Stimulus Frequency During Self-Paced Exercise and Recovery
Bibliographic record
Abstract
The aim of this study was to investigate the effect of potentiation on stimulation-induced muscle function during and after an intense bout of self-paced dynamic exercise. Ten active subjects performed a time trial involving repetitive concentric extension-flexion of the right knee using a Biodex dynamometer. Electrical stimulation before and after a 5 s maximal isometric voluntary contraction was performed before the start of the time trial and immediately (< 5 s) after each 20% of the time trial as well as 1, 2, 4 and 8 min after time trial termination. Potentiation was observed before the time trial and as early as 1-2 min after the time trial, but no potentiation was detected during or immediately after the time trial for neither single or paired stimuli. At termination of the time trial, "potentiated" peak torque was significantly more reduced than "unpotentiated" peak torque for single stimulus (-65 ± 10% and -42 ± 18%, respectively) and paired stimuli at 100 Hz (-51 ± 10% and -33 ± 15%, respectively). Faster recovery for "potentiated" compared to "unpotentiated" peak torque indicate that potentiate peak torque measurements or delay the post-exercise measurements more than a few seconds, will underestimate peripheral fatigue. In conclusion, the potentiation after maximal contraction disappears during intense exercise. Whether the muscle is already potentiated during intense contraction or fatiguing mechanisms inhibits potentiation remains to be clarified.
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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) | 0.001 | 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".