Performance parametric formulation of carbon fiber-reinforced composite locking bone implant plates based on finite-element analysis
Bibliographic record
Abstract
The treatment of Giant Cell Tumor (GCT) in the distal radius poses challenges due to the intricate anatomical features of the bone. It often necessitates the use of long implant plates or the interconnection of multiple short plates after tumor excision. However, the deployment of metal plates may increase the risk of screw loosening and various complications. To address these challenges, this study proposes the adoption of carbon fiber-reinforced PEEK (CFRP) as the base material. As a unique strategy, performance parameters (PP) were developed to compare CFRP implant plates with a Ti-6Al-4V plate using the Finite-element Method. The focus was on four elements: the screw axial force, bone growth, callus formation, and bone resorption. The investigation into the screw axial force involved analyzing the internal force of the screw. The remaining parameters were evaluated using the stress, strain, or elastic energy induced in the bones. The findings showed that the second screw endured the largest screw axial force, measuring 10.16 N under a 90-degree 10-N loading at the translocated bone. The model without a callus exerted a significantly greater force on the screw than the model with a callus, leading to screw loosening in the early stage of treatment. The maximum PP, reached 1.62, was achieved with an angle-ply [456/−456] laminate, featuring a weighting fraction of 0.7 for bone growth and 0.1 for the other parameters. This study provides a generalized methodology for assessing the performances of CFRP implants and offers guidelines for future development in composite implant plate technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".