Short interspike intervals and double discharges of anconeus motor unit action potentials for the production of dynamic elbow extensions
Bibliographic record
Abstract
Incidence of double discharges (DDs; >100 Hz) and short interspike intervals (ISIs; >50 to <100 Hz) is reported to vary widely among different muscles and tasks, with a higher incidence in motor unit (MU) trains of fast muscles and for the production of fast contractions in humans. However, it is unclear whether human muscles with a large composition of slower motor units exhibit DDs or short ISIs when activated with maximal synaptic drive, such as those required for maximal velocity dynamic contractions. Thus the purpose of this study was to determine the effect of increasing peak contraction velocity on the incidence of DDs and short ISIs in the anconeus muscle. Seventeen anconeus MUs in 10 young males were recorded across dynamic elbow extensions ranging from low submaximal velocities (16% of maximal velocity) up to maximal velocities. A low incidence of DDs (4%) and short ISIs (29%) was observed among the 583 MU trains recorded. Despite the low incidence in individual MU trains, a majority (71% and 94%, respectively) of MUs exhibited at least one DD or short ISI. The number of short ISIs shared no variance with MU recruitment threshold (R(2) = 0.02), but their distribution was skewed toward higher peak velocities (G = -1.26) and a main effect of peak elbow extension velocity was observed (P < 0.05). Although a greater number of short ISIs was observed with increasing velocity, the low incidence of DDs and short ISIs in the anconeus muscle is likely related to the function of the anconeus as a stabilizer rather than voluntary elbow extensor torque and velocity production.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".