Submaximal motor unit firing rates after 8 wk of isometric resistance training
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to test the hypothesis that average motor unit firing rates change in parallel with the contractile properties of vastus lateralis following 8 wk of isometric resistance training. METHODS: The firing rates from more than 400 motor units of vastus lateralis were obtained during voluntary isometric contractions of 50% MVC, before and again after training in male subjects (N = 10) and their untrained controls (N = 10). Single motor unit spike trains were recorded with tungsten microelectrodes. RESULTS: Training resulted in a 36% (P < 0.05) increase in MVC. We also found significant increases (P < 0.05) in maximal twitch amplitude (+17%), time to peak tension (+9%) and the maximal instantaneous rate of contraction (+20%) in the trained leg of the experimental group. Neither the maximal integrated EMG nor the rate of increase of integrated EMG was different after training. There were no significant changes in any of these measures from the untrained leg or the control group. Average firing rates were not different after training despite the increase in twitch contractile speed. CONCLUSION: These findings suggest that the control properties of the nervous system are not altered despite sizable changes in the contractile properties of muscle following 8 wk of resistance training.
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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 | 0.000 |
| 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) | 0.002 | 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".