An Injectable Glass Polyalkenoate Cement Engineered for Fracture Fixation and Stabilization
Bibliographic record
Abstract
Glass polyalkenoate cements (GPCs) have potential as bio-adhesives due to their ease of application, appropriate mechanical properties, radiopacity and chemical adhesion to bone. Aluminium (Al)-free GPCs have been discussed in the literature, but have proven difficult to balance injectability with mechanical integrity. For example, zinc-based, Al-free GPCs reported compressive strengths of 63 MPa, but set in under 2 min. Here, the authors design injectable GPCs (IGPCs) based on zinc-containing, Al-free silicate compositions containing GeO₂, substituted for ZnO at 3% increments through the series. The setting reactions, injectability and mechanical properties of these GPCs were evaluated using both a hand-mix (h) technique, using a spatula for sample preparation and application and an injection (i) technique, using a 16-gauge needle, post mixing, for application. GPCs ability to act as a carrier for bovine serum albumin (BSA) was also evaluated. Germanium (Ge) and BSA containing IGPCs were produced and reported to have working times between 26 and 44 min and setting times between 37 and 55 min; the extended handling properties being as a result of less Ge. The incorporation of BSA into the cement had no effect on the handling and mechanical properties, but the latter were found to have increased compression strength with the addition of Ge from between 27 and 37 MPa after 30 days maturation.
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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 | 0.000 |
| 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".