Trabecular bone density distribution in the scapula of patients undergoing reverse shoulder arthroplasty
Bibliographic record
Abstract
BACKGROUND: To improve implant survival after reverse shoulder arthroplasty (RSA), surgeons need to maximize screw fixation. However, bone density variation and distribution within the scapula are not well understood as they relate to RSA. The three columns of bone in the scapula surrounding the glenoid fossa are the lateral border, the base of the coracoid process, and the spine of the scapula. In our previous study by Daalder et al on cadaveric specimens, the coracoid column was significantly less dense than the lateral border and spine. This study's objective was to verify whether these results are consistent with computer tomography (CT) scan information from patients undergoing RSA. METHODS: Two-dimensional axial CT images from twelve patients were segmented, and a three-dimensional digital model of the scapula was subsequently created using Mimics 17.0 Materialise Software (Leuven, Belgium). Hounsfield unit (HU) values representing cortical bone were filtered out to determine the distributions of trabecular bone density. An analysis of variance with post hoc Bonferroni tests determined the differences in bone density between the columns of bone in the scapula. RESULTS: = 1.54). DISCUSSION/CONCLUSION: The higher-density bone in the spine and lateral border compared with the coracoid region may provide better bone purchase for screws when fixing the glenoid baseplate in RSA. This is in agreement with our previous study and indicates that the previous cadaveric results are applicable to clinical CT scan data. When these studies are taken together, they provide robust evidence for clinical applications, including having surgeons aim screws for higher-density regions to increase screw fixation, which may decrease micromotion and improve implant longevity.
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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 | 0.001 |
| 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) | 0.003 | 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".