Stabilizing effect of an elbow orthosis with an adjustable hinge axis after lateral collateral ligament injury: A biomechanical study
Bibliographic record
Abstract
Background: Current commercial elbow braces have a straight hinge that does not account for the native carrying angle of the elbow. The objective of this study was to determine the effectiveness of a custom-designed hinged elbow orthosis (HEO) with variable valgus angulations in stabilizing a lateral collateral ligament (LCL) deficient elbow. Methods: Eight cadaveric upper extremities were mounted in an elbow motion simulator in the abducted varus gravity-loaded position. The specimens were examined before and after simulated LCL injury and then with the addition of the custom-designed HEO with 0°, 10°, and 20° of valgus angulation. Kinematic data were recorded using an electromagnetic tracking system. Results: < 0.05). There were no significant differences in varus-valgus angulation or ulnohumeral rotation between any of the brace angles and the LCL injured state with the forearm pronated and supinated. Discussion: The custom-designed HEO did not provide any additional stability to the LCL injured elbow. The varus arm position should be avoided during the rehabilitation of an LCL injured elbow even when an HEO is used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".