Characterization of Posterior Glenoid Bone Loss
Bibliographic record
Abstract
Objectives: The purpose of this study was to characterize the morphology and location of posterior glenoid bone loss in pat ients with posterior instability instability utilizing computed tomography (CT). Methods: Clinical data was selected for patients with posterior shoulder instability that had undergone posterior stabilization (open or arthroscopic) or posterior osseous augmentation (distal tibia or iliac crest). Three fellowship-trained surgeons from two institutions contributed patients. Pre-operative CT data was collected for all patients. The axial cuts were segmented and reformatted in three-dimensions for glenoid analysis using Osirix. From this three-dimensional model, the following was calculated: percent bone loss (Nobuhara), total arc of the defect (degrees), and a clock-face description (start point, stop point, and average or direction). Pearson correlation coefficients were performed using significance of p<0.05. Results: Fifty shoulders from 50 patients were reviewed. Fourteen patients (average age 30 years; 93% male) had evidence of posterior glenoid bone loss and were included for evaluation. Defects on average involved 13.7±8.6% of the glenoid (range, 2-35%). The average start time (assuming all right shoulders) on the clock face was 10 o’clock ± 40 minutes and stopped at 6:30 ± 25 minutes. The average direction of the defect pointed toward 8:15 ± 25 minutes. The percent bone loss correlated with the total arc of the defect (Pearson: 0.93, p<0.05, R2: 0.86) and the direction of the bone loss (Pearson: 0.64, p<0.05, R2: 0.40). The direction of bone loss significantly moved more posterosuperior the larger the defect became (Pearson: 0.63, p<0.05, R2: 0.40). Conclusion: Posterior bone loss associated with posterior glenohumeral instability is typically directed posteriorly at 8:15 on the clock. As defect get bigger, this direction moves more posterosuperior. This information will help guide clinicians in understanding the typical location of posterior bone loss aiding in diagnosis, cadaveric models, and treatment.
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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 | 0.002 | 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) | 0.002 | 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".