Changes in motor unit recruitment thresholds of the human anconeus muscle during torque development preceding shortening elbow extensions
Bibliographic record
Abstract
Rate of torque development and the subsequent movement velocity are modulated by motor unit (MU) properties, primarily MU discharge rate and MU recruitment threshold (MURT). In isometric conditions, MURTs have been shown to decrease with increased rates of torque development. It is unclear whether this relationship is similar in the production of dynamic shortening contractions. Using fast joint velocities to drive the system, we aimed to determine how anconeus MURTs recorded during the torque production phase preceding movement were affected in relation to the resultant peak elbow extension velocity. Recruitment thresholds of 17 MUs from 9 young men were tracked throughout non-isokinetic dynamic elbow extensions with velocities ranging from 64°/s to 500°/s at a constant resistance of 25% of maximal voluntary isometric contraction and during isometric elbow extensions (0°/s). Relative MURTs decreased ∼50% from the slowest (<25% of maximal velocity) to the fastest (>75% of maximal velocity) resultant velocity ranges (P < 0.05). Although a significant (P < 0.001) but weak (r = -0.27, R(2) = 0.08) relationship was observed between MURT and resultant peak elbow extension velocity for the group, only 7 of the 17 MUs displayed significant moderate (r = -0.40, R(2) = 0.17) to strong (r = -0.85, R(2) = 0.73) negative MURT-velocity relationships. These data indicate variable responses of MURTs with increasing resultant peak velocity, which may be related to the intrinsic properties of individual MUs.
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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) | 0.001 | 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".