ANALYSIS OF HEALTHY SHOULDER GLENOHUMERAL ARTHROKINEMATICS USING 4D CT FOR INTERNAL ROTATION AND FORWARD ELEVATION
Bibliographic record
Abstract
The shoulder is the most mobile joint in the human body, leading it to be the one of the most dislocated joints in the human body and putting it at higher risk of developing pathologies (e.g., osteoarthritis (OA)), which increases the likelihood of a shoulder replacement [1]. It is unknown how exactly the healthy humeral head tracks relative to the glenoid, and how that changes overtime as people age. Determining the exact amount of GH joint proximity and translation in the healthy population, while also analyzing the variances in different ages, will allow clinical professionals to help with better prevention of injuries, early detection of joint asymmetries, abnormalities, and diseases, and improving treatment plans to help with better patient recovery. As well, it will allow for more optimal shoulder implants to be designed which will allow patients of all ages to move their shoulder normally. Thirty-one participants were recruited for this study and were split into two cohorts depending on age: young (45 years old). Four-dimensional computed tomography (4DCT) scans were taken as these participants completed two movements of everyday life (e.g., Internal Rotation to the back (IR) and Forward Elevation (FE)). 3D bone models of the humerus and scapula were created using 3D slicer for all patients. Using these 3D bone models, an inter-bone distance algorithm was used to determine GH joint proximity, and registration, using an ICP (Iterative Closest Point) algorithm was used to then determine GH joint tracking (translation). In total, there were two statistically significant results in the analysis of GH joint proximity and joint tracking (translation). Firstly, when examining GH joint proximity, old participants displayed closer joint proximity during the middle of the movement compared to the young participants (11.0% difference, p=0.039, Figure 1) for IR. Secondly, also for IR, young participants had more translation in the S/I direction (16.2%) compared to the A/P direction (10.4%) (5.8% difference total, p=0.016, Figure 2). This research allowed us to quantify GH joint proximity and joint surface tracking (translation) during IR and FE, which has not yet been established in literature. It is also vital in allowing shoulder implant designers to understand how the native healthy shoulder joint moves so that they can design shoulder implants that can allow for the important translational movement found in this study. For any figures or tables, please contact the authors directly.
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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 | 0.001 | 0.001 |
| 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".