Exploring age-related differences in asymptomatic male shoulder kinematics using four-dimensional computed tomography
Bibliographic record
Abstract
Background: Understanding age-related differences in shoulder glenohumeral and scapulothoracic motion has implications for the understanding, treatment, and management of shoulder injuries and diseases. Previous studies have investigated age-related differences, although statically and in single-plane motion. The shoulder, however, is a complex joint capable of a wide range of motion (ROM) and involves coordinated and synchronous movements from both the glenohumeral and scapulothoracic joints. Therefore, the objectives of this study were to measure age-related differences in kinematics of the glenohumeral and scapulothoracic joints during motion, as well as differences in the neutral positioning of the scapula and humerus. Methods: Thirty-one male participants comprised 2 cohorts based on age (<45 and ≥ 45 years). Participants performed 2 motions, forward elevation (FE) and internal rotation (IR) to the back, with 4-dimensional computed tomography scanning to dynamically track the bones. The kinematics of the humerus and scapula were calculated with 6 degrees of freedom. The neutral position of the scapula and humerus was also calculated based on a static computed tomography scan. Results: = .007). Conclusion: Overall, this study found age-related differences in kinematics and neutral positioning of the scapula and humerus, which may help improve understanding of age-related differences in subluxation, diseases, injuries, and ROM.
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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 | 0.001 | 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".