Voluntary activation and twitch potentiation of the elbow flexors across supinated, neutral, and pronated forearm orientations
Bibliographic record
Abstract
Elbow flexion force depends on forearm orientation with supinated and neutral being stronger than pronated. The purpose of this study was to assess the influence of forearm orientation on voluntary activation (VA), postactivation potentiation (PAP), and twitch properties. Eleven males (23 ± 3 years) performed isometric elbow flexion maximal voluntary contractions (MVC) in supinated, neutral, and pronated forearm orientations with supramaximal stimulation to the biceps brachii muscle belly before, during, and after the MVC. MVC and VA were higher in supinated (213.6 ± 49.6 N; 93.0 ± 5.2%) and neutral (243.6 ± 48.0 N; 96.1 ± 3.2%) compared with pronated (113.6 ± 21.3 N; 70.9 ± 20.4%) (P < 0.05), while PAP did not differ across the three orientations (71.6 ± 42.2%) (P > 0.05). In the rested state, pronated peak tension (PT) was less compared with supinated (42%). In the potentiated state, pronated PT was less than supinated (50%) and neutral (53%) (P < 0.05). Reduced strength in the pronated orientation is partially attributed to reduced drive; however, reductions in peak tension indicate that there also is a mechanical disadvantage when the forearm is placed into a pronated orientation, and this does not alter PAP.
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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".