Sex differences in motor unit discharge rates at maximal and submaximal levels of force output
Bibliographic record
Abstract
This study evaluated potential sex differences in motor unit (MU) behaviour at maximal and submaximal force outputs. Forty-eight participants, 24 females and 24 males, performed isometric dorsiflexion contractions at 20%, 40%, 60%, 80%, and 100% of a maximum voluntary contraction (MVC). Tibialis anterior electromyography was recorded both by surface and intramuscular electrodes. Compared with males, females had a greater MU discharge rate (MUDR) averaged across all submaximal intensities (Δ 0.45 pps, 2.56%). Males exhibited greater increases in MUDR above 40% MVC, surpassing females at 100% MVC (p’s < 0.01). Averaged across all force outputs, females had a greater incidence of doublet and rapid discharges and a greater percentage of MU trains with doublet and rapid (5–10 ms) discharges (Δ 75.55% and 61.48%, respectively; p’s < 0.01). A subset of males (n = 8) and females (n = 8), matched for maximum force output, revealed that females had even greater MUDR (Δ 1.38 pps, 7.47%) and percentage of MU trains with doublet and rapid discharges (Δ 51.62%, 56.68%, respectively; p’s < 0.01) compared with males at each force output, including 100% MVC. Analysis of the subset of strength-matched males and females suggest that sex differences in MU behaviour may be a result of females needing to generate greater neural drive to achieve fused tetanus. Novelty Females had higher MUDRs and greater percentage of MU trains with doublets across submaximal force outputs (20%–80% MVC). Differences were even greater for a strength matched subset. Differences in motor unit behaviour may arise from musculoskeletal differences, requiring greater neural drive in females.
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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) | 0.004 | 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".