Reliability of transcranial magnetic stimulation-evoked responses on knee extensor muscles during cycling
Bibliographic record
Abstract
ABSTRACT Transcranial magnetic stimulation (TMS) measures the excitability and inhibition of corticomotor networks. Despite its task-specificity, few studies have used TMS during dynamic movements and the reliability of TMS-derived measures has not been assessed during cycling. This study aimed to evaluate the reliability of motor evoked potentials (MEP) and short- and long-interval intracortical inhibition (SICI and LICI) on vastus lateralis and rectus femoris muscle activity during a fatiguing single-leg cycling task. Nine healthy adults (2 females) performed two identical sessions of counterweighted single-leg cycling at 60% peak power output until failure. Five single-pulses and five short- and long-interval paired pulses delivered to the motor cortex, and two maximal femoral nerve stimulations [maximal M-wave (M max )], were delivered during two baseline cycling bouts (unfatigued) and every 5 min throughout cycling (fatigued). When comparing both baseline bouts within the same session, MEP·M max -1 and LICI (both ICC: >0.9) were rated excellent while SICI was rated good (ICC: 0.7-0.9). At baseline between sessions, in the vastus lateralis, M max (ICC: >0.9) and MEP·M max -1 (ICC: 0.7) demonstrated good reliability, LICI was moderate (ICC: 0.5), and SICI was poor (ICC: 0.3). Across the fatiguing task, M max demonstrated excellent reliability (ICC >0.8), MEP·M max -1 ranged good to excellent (ICC: 0.7-0.9), LICI was moderate to excellent (ICC: 0.5-0.9), and SICI remained poorly reliable (ICC: 0.3-0.6). Overall, these results corroborate the cruciality of retaining mode-specific testing measurements and suggest that during cycling, M max , MEP·M max -1 , and LICI measures are reliable whereas SICI, although less reliable across days, can be reliable within the same session.
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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 | 0.004 |
| 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".