Comparative Evaluation of Two Glass Polyalkenoate Cements: An In Vivo Pilot Study Using a Sheep Model
Bibliographic record
Abstract
Poly(methyl methacrylate) (PMMA) is used to manage bone loss in revision total knee arthroplasty (rTKA). However, the application of PMMA has been associated with complications such as volumetric shrinkage, necrosis, wear debris, and loosening. Glass polyalkenoate cements (GPCs) have potential bone cementation applications. Unlike PMMA, GPC does not undergo volumetric shrinkage, adheres chemically to bone, and does not undergo an exothermic setting reaction. In this study, two different compositions of GPCs (GPCA and GPCB), based on the patented glass system SiO2-CaO-SrO-P2O5-Ta2O5, were investigated. Working and setting times, pH, ion release, compressive strength, and cytotoxicity of each composition were assessed, and based on the results of these tests, three sets of samples from GPCA were implanted into the distal femur and proximal tibia of three sheep (alongside PMMA as control). Clinical CT scans and micro-CT images obtained at 0, 6, and 12 weeks revealed the varied radiological responses of sheep bone to GPCA. One GPCA sample (implanted in the sheep for 12 weeks) was characterized with no bone resorption. Furthermore, a continuous bone–cement interface was observed in the CT images of this sample. The other implanted GPCA showed a thin radiolucent border at six weeks, indicating some bone resorption occurred. The third sample showed extensive bone resorption at both six and 12 weeks. Possible speculative factors that might be involved in the varied response can be: excessive Zn2+ ion release, low pH, mixing variability, and difficulty in inserting the samples into different parts of the sheep bone.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".