Electromyographic activity and strength during maximum isometric pronation and supination efforts in healthy adults
Bibliographic record
Abstract
There exists a lack of quantitative data in the literature related to the torque produced during axial forearm rotation and the electromyographic (EMG) activity of the muscles involved. Therefore, the purpose of this study was to compare the relative EMG activity of four forearm muscles during resisted forearm rotation. A custom-built device capable of measuring torque in the absence of grip was employed. Fourteen healthy volunteers performed maximum isometric voluntary contractions in five positions of axial forearm rotation for both pronation and supination. EMG data were collected simultaneously from the supinator, biceps, pronator quadratus (deep and superficial heads), and pronator teres muscles using fine-wire bipolar electrodes. Data were analyzed to determine the contributions of each muscle to pronation and supination torque over five positions of forearm rotation. In the absence of grip no significant difference was found between supination and pronation torque in neutral position. Supination torque generation was greater in the pronated forearm positions, and pronation torque was greater in the supinated positions (p<0.05). A root-mean-square EMG analysis verified the major contributions of the pronator teres and both heads of the pronator quadratus muscle to pronation torque, and supinator and biceps to supination torque. The deep head of the pronator quadratus was active during both pronation and supination, lending support to the theory that it may act primarily as a dynamic distal radioulnar joint stabilizer. This information may be helpful in upper extremity modeling, surgical treatments, and rehabilitation strategies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".