Rotating hinge knee causes lower bone–implant interface stress compared to constrained condylar knee replacement
Bibliographic record
Abstract
PURPOSE: To compare the stresses at bone-arthroplasty interface of constrained and semi-constrained knee prostheses, using the finite element (FE) method as a predictor of the survivorship of the implants. METHODS: Three-dimensional FE models of the knee implanted with rotating hinge (RHK) and legacy constrained condylar (LCCK) prostheses were generated to study the loads and stresses for two situations: medial- and lateral collateral ligament deficiencies in full extension. RESULTS: On average, the shear stress developed at bone-implant interface dropped from 16.9 to 13.7 MPa (18.9%), and the interface von Mises stress lowered from 37.6 to 30.2 MPa (19.6%) in RHK compared to those in LCCK prostheses. RHK design also resulted in a more uniform stress distribution at the interfaces in both femur and tibia. The average polyethylene liner stress dropped from 9.6 to 2.6 MPa (a 72.7% decrease) in RHK design when compared to that in LCCK design. CONCLUSION: The more uniform interface stress suggests fewer density changes at the periprosthetic regions due to bone remodelling. Moreover, the lower polyethylene stresses are likely to reduce wear and damage. These findings reveal that the RHK design may have more favorable mechanical features compared to LCCK design in full extension boundary conditions, implying a potentially better survivorship. However, the findings should be interpreted cautiously as other configurations were not investigated.
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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 | 0.000 | 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) | 0.002 | 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".