A novel method for localization of the maximum glenoid bone defect during reverse shoulder arthroplasty
Bibliographic record
Abstract
BACKGROUND: Management of glenoid bone defects during reverse shoulder arthroplasty remains a challenge. The aim of our study was to preoperatively localize the maximal depth of glenoid bone defects in relation to glenoid reaming. METHODS: Thirty preoperative shoulder computed tomography scans were collected. Three assessors created standardized surgical plans, using 3-dimensional (3D) computed tomography-based Blueprint planning software in which the reaming axis was held constant at zero degrees of version and inclination. Each plan resulted in a 2-dimensional (2D) image of the reamer's contact on the glenoid and a corresponding 3D representation of the glenoid bone defect. The position of the maximum glenoid defect was localized on both the 2D and 3D images. Descriptive statistics were calculated. The correlation between angles from 2D and 3D images was assessed, and intraclass correlation was used to assess inter-rater and intrarater reliability. RESULTS: Twenty-eight patients were included. The overall mean difference between 2D and 3D angles was 5.4° (standard deviation 5.2°). The correlation between 2D and 3D angles was almost perfect. Intraclass correlation results demonstrated near-perfect agreement. The maximal glenoid defect was within 5% of a circle (or +/- 9°) from perpendicular to the high-side ream line in 85.1% of comparisons and was within 10% of a circle in 97.6% of comparisons. CONCLUSION: Using Blueprint planning software, we have demonstrated with almost perfect agreement among 3 assessors that when the reaming axis is held constant, the maximum glenoid bone defect is reliably located perpendicular to the glenoid ream line.
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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 | 0.000 | 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) | 0.000 | 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".