Trabecular bone density distribution in the scapula relevant to reverse shoulder arthroplasty
Bibliographic record
Abstract
BACKGROUND: How trabecular bone density varies within the scapula and how this may lead to more optimal reverse shoulder arthroplasty (RSA) screw placement has not been addressed in the scientific literature. The 3 columns of trabecular bone within the scapula adjacent to the glenoid fossa, one extending through the lateral border, a second into the base of the coracoid process, and a third extending into the spine of the scapula, were hypothesized to be of relatively similar density. METHODS: Two-dimensional axial computed tomography (CT) images of 19 fresh frozen cadaver specimens were obtained. Digital Imaging and Communications in Medicine (DICOM; National Electrical Manufacturers Association, Rosslyn, VA, USA) image files of the CT scanned scapulae were imported into Mimics 17.0 Materialise Software (Leuven, Belgium) for segmentation and 3-dimensional digital model generation. To determine the distribution of trabecular bone density, Hounsfield unit (HU) values in the scapulae gray value files obtained from Mimics were filtered to remove any cortical bone. HU values of 650 define the corticocancellous interface in CT image data and were considered to be cortical bone. Analyses of variance with post hoc Bonferroni tests were used to determine statistical differences between the intra- and inter-regions of bone density comparisons. RESULTS: < .05). DISCUSSION/CONCLUSION: The higher-quality bone in the spine and lateral border, compared with the coracoid regions, may provide better bone purchase for screws when fixing the glenoid baseplate in RSA.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".