Neuromuscular recruitment strategies of the vastus lateralis according to sex
Bibliographic record
Abstract
AIM: Despite males typically exhibiting greater muscle strength and fatigability than females, it remains unclear if there are sex-based differences in neuromuscular recruitment strategies e.g. recruitment and modulation of motor unit firing rate (MU FR) at normalized forces and during progressive increases in force. METHODS: The study includes 29 healthy male and 31 healthy female participants (18-35 years). Intramuscular electromyography (iEMG) was used to record individual motor unit potentials (MUPs) and near-fibre MUPs from the vastus lateralis (VL) during 10% and 25% maximum isometric voluntary contractions (MVC), and spike-triggered averaging was used to obtain motor unit number estimates (MUNE) of the VL. RESULTS: Males exhibited greater muscle strength (P < .001) and size (P < .001) than females, with no difference in force steadiness at 10% or 25% MVC. Females had 8.4% and 6.5% higher FR at 10% and 25% MVC, respectively (both P < .03), while the MUP area was 33% smaller in females at 10% MVC (P < .02) and 26% smaller at 25% MVC (P = .062). However, both sexes showed similar increases in MU size and FR when moving from low- to mid-level contractions. There were no sex differences in any near-fibre MUP parameters or in MUNE. CONCLUSION: In the vastus lateralis, females produce muscle force via different neuromuscular recruitment strategies to males which is characterized by smaller MUs discharging at higher rates. However, similar strategies are employed to increase force production from low- to mid-level contractions. These findings of similar proportional increases between sexes support the use of mixed sex cohorts in studies of this nature.
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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) | 0.003 | 0.001 |
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".