Effect of implant design and bioactive glass coating on biomechanical properties of fiber‐reinforced composite implants
Bibliographic record
Abstract
This study aimed to evaluate the influence of implant design and bioactive glass (BAG) coating on the response of bone to fiber-reinforced composite (FRC) implants. Three different FRC implant types were manufactured for the study: non-threaded implants with a BAG coating; threaded implants with a BAG coating; and threaded implants with a grit-blasted surface. Thirty-six implants (six implants for each group per time point) were installed in the tibiae of six pigs. After an implantation period of 4 and 12 wk, the implants were retrieved and prepared for micro-computed tomography (micro-CT), push-out testing, and scanning electron microscopy analysis. Micro-CT demonstrated that the screw-threads and implant structure remained undamaged during the installation. The threaded FRC/BAG implants had the highest bone volume after 12 wk of implantation. The push-out strengths of the threaded FRC/BAG implants after 4 and 12 wk (463°N and 676°N, respectively) were significantly higher than those of the threaded FRC implants (416°N and 549°N, respectively) and the nonthreaded FRC/BAG implants (219°N and 430°N, respectively). Statistically significant correlation was found between bone volume and push-out strength values. This study showed that osseointegrated FRC implants can withstand the static loading up to failure without fracture, and that the addition of BAG significantly improves the push-out strength of FRC implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".