Muscle Forces and Pronation Stabilize the Lateral Ligament Deficient Elbow
Bibliographic record
Abstract
The influence of muscle activity and forearm position on the stability of the lateral collateral ligament deficient elbow was investigated in vitro, using a custom testing apparatus to simulate active and passive elbow flexion. Rotation of the ulna relative to the humerus was measured before and after sectioning of the joint capsule, and the radial and lateral ulnar collateral ligaments from the lateral epicondyle. Gross instability was present after lateral collateral ligament transection during passive elbow flexion with the arm in the varus orientation. In the vertical orientation during passive elbow flexion, stability of the lateral collateral ligament deficient elbow was similar to the intact elbow with the forearm held in pronation, but not similar to the intact elbow when maintained in supination. This instability with the forearm supinated was reduced significantly when simulated active flexion was done. The stabilizing effect of muscle activity suggests physical therapy of the lateral collateral ligament deficient elbow should focus on active rather than passive mobilization, while avoiding shoulder abduction to minimize varus elbow stress. Passive mobilization should be done with the forearm maintained in pronation.
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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) | 0.002 | 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".