Use of transcranial magnetic stimulation to assess relaxation rates in unfatigued and fatigued knee-extensor muscles
Bibliographic record
Abstract
Abstract We examined whether transcranial magnetic stimulation (TMS) delivered to the motor cortex allows assessment of muscle relaxation rates in unfatigued and fatigued knee extensors (KE). We assessed the ability of this technique to measure time course of fatigue-induced changes in muscle relaxation rate and compared relaxation rate from resting twitches evoked by femoral nerve stimulation. Twelve healthy men performed maximal voluntary isometric contractions (MVC) twice before (PRE) and once at the end of a 2-min KE MVC and five more times within 8 min during recovery. Relative (intraclass correlation coefficient; ICC2,1) and absolute (repeatability coefficient) reliability and variability (coefficient of variation) were assessed. Time course of fatigue-induced changes in muscle relaxation rate was tested with generalized estimating equations. In unfatigued KE, peak relaxation rate coefficient of variation and repeatability coefficient were similar for both techniques. Mean (95% CI) ICC2,1 for peak relaxation rates were 0.933 (0.724–0.982) and 0.889 (0.603–0.968) for TMS and femoral nerve stimulation, respectively. TMS-induced normalized muscle relaxation rate was − 11.5 ± 2.5 s−1 at PRE, decreased to − 6.9 ± 1.2 s−1 (− 37 ± 17%, P < 0.001), and recovered by 2 min post-exercise. Normalized peak relaxation rate for resting twitch did not show a fatigue-induced change. During fatiguing KE exercise, the change in muscle relaxation rate as determined by the two techniques was different. TMS provides reliable values of muscle relaxation rates. Furthermore, it is sufficiently sensitive and more appropriate than the resting twitch evoked by femoral nerve stimulation to reveal fatigue-induced changes in KE.
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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 | 0.003 |
| 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 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".