Analysis of healthy glenohumeral arthrokinematics using four-dimensional computed tomography throughout internal rotation and forward elevation
Bibliographic record
Abstract
Background: The glenohumeral (GH) joint is the most mobile joint in the human body and can translate, as well as rotate in its socket. Currently, it is not well established in literature how much the healthy humeral head translates, and how that changes overtime as people age. The objective of this study is to quantify GH joint proximity and translation in healthy participants and determine if there are any age-related, position-related, or direction-related differences. Methods: Thirty-one participants were recruited for this study and split into 2 cohorts: young (aged ≤ 37 years) and old (aged ≥ 45 years). Four-dimensional computed tomography scans were taken as these participants completed internal rotation (IR) to the back and forward elevation. Three-dimensional bone models of the humerus and scapula were created using 3D Slicer. An interbone distance algorithm and an iterative closest point algorithm were used to determine GH joint proximity and translation, respectively. Results: This study found that older participants displayed significantly closer joint proximity (63% of glenoid surface was within 4 mm of humeral head) during the middle of IR, compared to younger participants (52% of glenoid surface within 4 mm of humeral head). Additionally, younger participants had significantly more translation in the superior/inferior direction (16% of glenoid height) compared to the anterior/posterior direction (10% of glenoid width) throughout IR. Conclusion: This study demonstrates the significance of translational movements within the GH joint throughout IR and forward elevation, which will aid implant manufacturers in designing implants that will allow for more normalized GH translations.
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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) | 0.001 | 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".