Measuring Bone Loss in the Unstable Shoulder: Understanding and Applying the Track Concept
Bibliographic record
Abstract
An interesting international debate has been developed over the past 10 years (the last decade) surrounding the surgical procedure for recurrent anteroinferior instability and a definitive consensus is lacking on the factors which favor one technique over another, especially when bone loss is present (soft tissue vs. bone block). Glenoid bone loss is commonly observed in the shoulder with anterior instability, and it is difficult to evaluate the shape of the glenoid using plain radiograph, therefore, computed tomography or intraoperative observation is recommended for accurate assessment of glenoid bone loss and Hill-Sachs lesion. When we consider the bony defect of the glenoid as a risk factor for surgical failure, it is crucial to take into consideration the features of a concomitant Hill-Sachs lesion. However, all the previous reports focusing on the size of the Hill-Sachs lesion or on the glenoid bone loss in isolation, overlook the interaction of the 2 lesions through the arc of range of motion and how this may influence instability. The glenoid track is the first model to determine, in a dynamic way, how bone loss on both sides of the joint can lead to instability. The glenoid track is a zone of contact created by the glenoid on the humeral articular surface when the arm is moved along the end-range of motion (abduction and external rotation). The use of the glenoid track concept can potentially help guide surgical decision-making.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".