Structural analysis of hollow versus solid‐stemmed shoulder implants of proximal humeri with different bone qualities
Bibliographic record
Abstract
Stress shielding of the proximal humerus following total shoulder arthroplasty (TSA) can promote unfavorable bone remodeling, especially for osteoporotic patients. The objective of this finite element (FE) study was to determine if a hollow, rather than solid, titanium stem can mitigate this effect for healthy, osteopenic, and osteoporotic bone. Using a population-based model of the humerus, representative average healthy, osteopenic, and osteoporotic humerus FE models were created. For each model, changes in bone and implant stresses following TSA were evaluated for different loading scenarios and compared between solid versus hollow-stemmed implants. For cortical bone, using an implant decreased von Mises stress with respect to intact values up to 34.4%, with a more pronounced effect at more proximal slices. In the most proximal slice, based on changes in strain energy density, hollow-stemmed implants outperformed solid-stemmed ones through reducing cortical bone volume with resorption potential by 11.7% ± 2.1% (p = .01). For cortical bone in this slice, the percentage of bone with resorption potential for the osteoporotic bone was greater than the healthy bone by 8.0% ± 1.4% using the hollow-stemmed implant (p = .04). These results suggest a small improvement in bone-implant mechanics using hollow-stemmed humeral implants and indicate osteoporosis could exacerbate stress shielding to some extent. The hollow stems maintained adequate strength and using even thinner walls may further reduce stress shielding. After further developing these models, future studies could yield optimized implant designs tuned for varying bone qualities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".