The Effect of Inhomogeneous Trabecular Stiffness Relationship Selection on Finite Element Outcomes for Shoulder Arthroplasty
Bibliographic record
Abstract
An important feature of humeral orthopedic finite element (FE) models is the trabecular stiffness relationship. These relationships depend on the anatomic site from which they are derived; but have not been developed for the humerus. As a consequence, humeral FE modeling relies on relationships for other anatomic sites. The variation in humeral FE outcomes due to the trabecular stiffness relationship is assessed. Stemless arthroplasty FE models were constructed from CT scans of eight humeri. Models were loaded corresponding to 45 deg and 75 deg abduction. Each bone was modeled five times with the only variable being the trabecular stiffness relationship: four derived from different anatomic-sites and one pooled across sites. The FE outcome measures assessed were implant-bone contact percentage, von Mises of the change in stress, and bone response potential. The variance attributed to the selection of the trabecular stiffness relationship was quantified as the standard deviation existing between models of different trabecular stiffness. Overall, variability due to changing the trabecular stiffness relationship was low for all humeral FE outcome measures assessed. The variability was highest within the stress and bone formation potential outcome measures of the trabecular region. Variability only exceeded 10% in the trabecular stress change within two of the eight slices evaluated. In conclusion, the low variations attributable to the selection of a trabecular stiffness relationship based on anatomic-site suggest that FE models constructed for shoulder arthroplasty can utilize an inhomogeneous site-pooled trabecular relationship without inducing marked variability in the assessed outcome measures.
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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 | 0.001 | 0.001 |
| 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".