Muscle Contributions to Elbow Joint Rotational Stiffness in Preparation for Sudden External Arm Perturbations
Bibliographic record
Abstract
Understanding joint stiffness and stability is beneficial for assessing injury risk. The purpose of this study was to examine joint rotational stiffness for individual muscles contributing to elbow joint stability. Fifteen male participants maintained combinations of three body orientations (standing, supine, sitting) and three hand preloads (no load, solid tube, fluid filled tube) while a device imposed a sudden elbow extension. Elbow angle and activity from nine muscles were inputs to a biomechanical model to determine relative contributions to elbow joint rotational stiffness, reported as percent of total stiffness. A body orientation by preload interaction was evident for most muscles (P<.001). Brachioradialis had the largest change in contribution while standing (no load, 18.5%; solid, 23.8%; fluid, 26.3%). Across trials, the greatest contributions were brachialis (30.4±1.9%) and brachioradialis (21.7±2.2%). Contributions from the forearm muscles and triceps were 5.5±0.6% and 9.2±1.9%, respectively. Contributions increased at time points closer to the perturbation (baseline to anticipatory), indicating increased neuromuscular response to resist rotation. This study quantified muscle contributions that resist elbow perturbations, found that forearm muscles contribute marginally and showed that orientation and preload should be considered when evaluating elbow joint stiffness and safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".