VALIDATING INTERNAL DENSITY CALIBRATION IN THE PROXIMAL HUMERUS TO ESTIMATE BONE STIFFNESS FOR STEMLESS SHOULDER ARTHROPLASTY
Bibliographic record
Abstract
INTRODUCTION Stemless humeral head components have emerged as a popular choice for patients undergoing shoulder arthroplasty for end-stage OA since they preserve non-diseased bone for future surgical revisions. Current pre-operative clinical measures are limited in assessing volumetric bone mineral density (vBMD) and mechanical properties in the region of bone directly supporting the component. Gold-standard phantom calibration, used to determine vBMD in CT images, is seldom utilized in clinical practice requiring alternative density measures for accurate vBMD. Internal density calibration using internal tissues as references has yet to be validated in the proximal humerus, and vBMD values have yet to be linked to finite element model (FEM) estimated stiffness in the context of stemless shoulder arthroplasty. OBJECTIVE 1) To determine the correlation between vBMD and finite element model (FEM) estimated stiffness 2) To determine the bias in internal density-based vBMD using three different referent tissue combinations compared to phantom-based vBMD, in the proximal humerus. METHODS Non-pathologic cadaveric single-energy CT images (n = 25), containing a K 2 HPO 4 phantom, were used to analyze a 10 mm region directly below the anatomic neck. Phantom-based vBMD was calculated for each region and used as input to image-based FEMs (ROD). Internal calibration used air (A), adipose (A), skeletal muscle (M), and cortical bone (C) to generate calibrated images from three different referent tissue combinations (AACM, ACM, AAC). Images were used to generate FEMs for each tissue combination. Results were compared between vBMD (mg K 2 HPO 4 /cc) and apparent modulus (E app ) for each internal calibration tissue combination to the phantom calibration using linear regression. Bland-Altman analysis was used to determine the agreement between tissue combination and phantom calibration for estimated stiffness values (E app ). RESULTS Linear regression (Figure 1) showed strong correlations between estimated stiffness and vBMD values for each calibration method (AACM R 2 = 0.7524; ACM R 2 = 0.7723; AAC R 2 = 0.7384; ROD R 2 = 0.7854) and slopes not significantly different from 1 (p < 0.001). Bland-Altman analysis (Figure 2) revealed the ACM tissue combination had the lowest error bounds in apparent modulus, compared to phantom-vBMD derived FEMs, with a mean bias of 80.15 MPa and 95% limits of agreement ranging from -164.55 to 324.86 MPa. CONCLUSION The results of this study support the use of internal density calibration as a valid method for using internal density calibrated images as input to FEMs for estimating stiffness in the proximal humerus. The ACM tissue combination provided the highest agreement with the gold standard phantom calibration. This internal density calibration method may provide a solution for determining vBMD in patients undergoing shoulder arthroplasty for end-stage OA where phantoms are not present in the CT image. By linking bone density measures with estimated stiffness values, the mechanical properties of bone in the region supporting the humeral component are considered, which has the potential to improve preoperative planning for stemless shoulder arthroplasty. Next steps are to apply the ACM internal calibration method and estimated stiffness values in retrospective CT images from patients who have undergone shoulder arthroplasty for end-stage OA (n = 88) to link surgical outcomes to stiffness measures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".