Biomechanical Validation of Finite Element Models for Two Silicone Metacarpophalangeal Joint Implants
Bibliographic record
Abstract
Silicone implants are used for prosthetic arthroplasty of metacarpophalangeal (MCP) joints severely damaged by rheumatoid arthritis. Different silicone elastomer MCP implant designs have been developed, including the Swanson and the NeuFlex implants. The goal of this study was to compare the in vitro mechanical behavior of Swanson and NeuFlex MCP joint implants. Three-dimensional (3D) finite element (FE) models of the silicone implants were modeled using the commercial software ANSYS and subjected to angular displacement from 0 deg to 90 deg. FE models were validated using mechanical tests of implants incrementally bent from 0 deg to 90 deg in a joint simulator. Swanson size 2 and 4 implants were compared with NeuFlex size 10 and 30 implants, respectively. Good agreement was observed throughout the range of motion for the flexion bending moment derived from 3D FE models and mechanical tests. From 30 deg to 90 deg, the Swanson 2 demonstrated a greater resistance to deformation than the NeuFlex 10 and required a greater bending moment for joint flexion. For larger implant sizes, the NeuFlex 30 had a steeper moment-displacement curve, but required a lower moment than the Swanson 4, due to implant preflexion. On average, the stress generated at the implant hinge from 30 deg to 90 deg was lower in the NeuFlex than in the Swanson. On average, starting from the neutral position of 30 deg for the preflexed NeuFlex implant, higher moments were required to extend the NeuFlex implants to 0 deg compared with the Swanson implants, which returned spontaneously to resting position. Implant toggling within the medullary canals was less in the NeuFlex than in the Swanson. The differential performance of these implants may be useful in implant selection based on the preoperative condition(s) of the joint and specific patient functional needs.
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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) | 0.001 | 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".