Sex‐related differences in corticospinal excitability outcome measures of the biceps brachii during a submaximal elbow flexor contraction
Bibliographic record
Abstract
Abstract The purpose of this study was to investigate the effects of sex, muscle thickness, and subcutaneous fat thickness (SFT) on corticospinal excitability outcome measures of the biceps brachii. Eighteen participants (10 males and 8 females) completed this study. Ultrasound was used to assess biceps brachii muscle thickness and the overlying SFT. Transcranial magnetic stimulation (TMS) was used to determine corticospinal excitability by inducing motor‐evoked potentials (MEPs) at eight different TMS intensities from 90% to 160% of active motor threshold (AMT) from the biceps brachii during an isometric contraction of the elbow flexors at 10% of maximum voluntary contraction (MVC). Biceps brachii maximal compound muscle action potential (Mmax) was also recorded prior to and after TMS. Males had higher (p < 0.001) biceps brachii muscle thickness and lower SFT, produced higher levels of MVC force and had, on average, higher (p < 0.001) MEP amplitudes at lower (p < 0.05) percentages of maximal stimulator output than females during the 10% elbow flexion MVC. Multiple linear regression modeling revealed that sex was not associated with any of the neurophysiological parameters examined, while SFT showed a positive association with the stimulation intensity required at AMT (p = 0.035) and a negative association with biceps brachii pre‐stimulus electromyography (EMG) activity (p = 0.021). Additionally, there was a small positive association between muscle thickness and biceps brachii pre‐stimulus EMG activity (p = 0.049). Overall, this study suggests that some measures of corticospinal excitability may be different between the sexes and influenced by SFT and muscle thickness.
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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 | 0.002 |
| 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) | 0.003 | 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".