The effect of humeral implant thickness and canal fill on interface contact and bone stresses in the proximal humerus
Bibliographic record
Abstract
BACKGROUND: Stem size is an important element for successful time zero primary fixation of a press-fit humeral stem in shoulder arthroplasty. Little basic science research, however, has been conducted on the effects of implant thickness and canal fill on load transfer, contact, and stress shielding. The purpose of this finite element study was to determine the effects of varying stem thickness on bone contact, bone stresses, and bone resorption owing to stress shielding. METHODS: Three generic short-stem implant models were developed and varied based on cross-sectional thickness (thinner - 8 mm, medium - 12 mm, thicker - 16 mm). Using a finite element model, three outcome measures were determined (1) the amount of bone-to-implant contact, (2) changes in cortical and trabecular bone stresses from the intact state, and (3) changes in cortical and trabecular strain energy densities which can predict bone remodeling or stress shielding. RESULTS: < .002). In addition, the thinner implant resulted in a substantially lower volume of bone predicted to stress shield and resorb when compared with the medium and thicker stems. DISCUSSION: The results demonstrate that thinner implants and lower canal fill may be beneficial over thicker sizes, provided equal initial fixation can be achieved. The thinner implant has a greater degree of load sharing and increases the mechanical load placed on surrounding bone, reducing the risk of stress shielding and bone resorption.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".