Influence of Porosity on Fracture Toughness and Fracture Behavior of Antibiotic-Loaded PMMA Bone Cement
Bibliographic record
Abstract
Aseptic loosening is the most common reason for the long-term revision of cemented arthroplasties with fracture of the cement being a postulated cause or contributing factor. In our previous studies we showed that adding an antibiotic to a polymethylmethacrylate (PMMA) bone cement led to detrimental effects on various mechanical properties of the cement such as bending strength, compressive strength and fracture toughness (KIC). This finding implied that the mechanical failure of antibiotic-loaded PMMA bone cement was influenced by its pore volume fraction. Up to now this aspect has not been studied. Hence the purposes of this study were to determine (1) the influence of antibiotic (telavancin) loading on the KIC of a widely used PMMA bone cement brand (Palacos®R) and (2) the influence of pore size and pore distribution on the fracture behavior of the KIC specimens. For (2) both experimental and numerical methods (extended finite element method [XFEM]) were used allowing a comparison between the two sets of results. We found that: (1) KIC decreased with increased porosity with the drop (relative to the value for the control cement) being significant when the telavancin loading was 4.8 wt/wt % (2 g of telavancin added to 40 g of control cement powder); (2) there was a critical pore size above which there was a significant decrease in KIC and is 1 mm; (3) crack propagation was strongly influenced by pore size and pore locations (pore-pore interactions); and, (4) there was good agreement between the experimental and XFEM results. The implications of these findings for the use of a telavancin-loaded PMMA bone cement in cemented total joint arthroplasties are commented upon.
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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) | 0.001 | 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".