The Role of Poly(Methyl Methacrylate) in Management of Bone Loss and Infection in Revision Total Knee Arthroplasty: A Review
Bibliographic record
Abstract
Poly(methyl methacrylate) (PMMA) is widely used in joint arthroplasty to secure an implant to the host bone. Complications including fracture, bone loss and infection might cause failure of total knee arthroplasty (TKA), resulting in the need for revision total knee arthroplasty (rTKA). The goals of this paper are: (1) to identify the most common complications, outside of sepsis, arising from the application of PMMA following rTKA, (2) to discuss the current applications and drawbacks of employing PMMA in managing bone loss, (3) to review the role of PMMA in addressing bone infection following complications in rTKA. Papers published between 1970 to 2018 have been considered through searching in Springer, Google Scholar, IEEE Xplore, Engineering village, PubMed and weblinks. This review considers the use of PMMA as both a bone void filler and as a spacer material in two-stage revision. To manage bone loss, PMMA is widely used to fill peripheral bone defects whose depth is less than 5 mm and covers less than 50% of the bone surface. Treatment of bone infections with PMMA is mainly for two-stage rTKA where antibiotic-loaded PMMA is inserted as a spacer. This review also shows that using antibiotic-loaded PMMA might cause complications such as toxicity to surrounding tissue, incomplete antibiotic agent release from the PMMA, roughness and bacterial colonization on the surface of PMMA. Although PMMA is the only commercial bone cement used in rTKA, there are concerns associated with using PMMA following rTKA. More research and clinical studies are needed to address these complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".