Rate modulation of human anconeus motor units during high-intensity dynamic elbow extensions
Bibliographic record
Abstract
Investigations of high-intensity isometric fatiguing protocols report decreases in motor unit firing rates (MUFRs), but little is known regarding changes in MUFRs following fatigue induced by high-intensity dynamic contractions. Our purpose was to evaluate MUFRs of the anconeus (an accessory elbow extensor) and elbow extension power production as a function of time to task failure (TTF) during high-velocity fatiguing concentric contractions against a moderately heavy resistance. Fine-wire intramuscular electrode pairs were inserted into the anconeus to record MUs in 12 male participants (25 ± 3 yr), over repeated sessions on separate days. MUs were tracked throughout a three-stage, varying load dynamic elbow extension protocol designed to extend the task duration for >1 min thereby inducing substantial fatigue. Mean MUFRs and peak power were calculated for three relative time ranges: 0-15% TTF (beginning), 45-60% TTF (middle) and 85-100% TTF (end). Mean duration of the overall fatigue protocol was ∼80 s. Following the protocol, isometric maximum voluntary contraction (MVC), highest velocity at 35% MVC load, and peak power decreased 37, 60, and 64% compared with baseline, respectively. Data from 20 anconeus MUs tracked successfully throughout the protocol indicated a reduction in MUFRs in relation to power loss from 36 Hz/160 W (0-15% TTF) to 28 Hz/97 W (45-60% TTF) to 23 Hz/43 W (85-100% TTF). During these high-intensity maximal effort concentric contractions, anconeus MUFRs decreased substantially (>35%). Although the absolute MUFRs were higher in the present study than those reported previously for other muscles during sustained high-intensity isometric tasks, the relative decrease in MUFRs was similar between the two tasks.
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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) | 0.001 | 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".