Peri‐implant alveolar bone resorption in an innovative peri‐implantitis murine model: Effect of implant surface and onset of infection
Bibliographic record
Abstract
PURPOSE: To compare the difference in alveolar bone resorption around implants after immediate placement in a bacterial induced experimental periimplantitis murine model. The various conditions that were examined were: Effect of implant surface characteristics and the onset of the induced infection. MATERIALS AND METHODS: Screw-shaped titanium implants, smooth-surface or sand-blasted large-grit acid-etched (SLA) coated, were inserted immediately after extraction of the first upper left molar, in 90 5-6-week-old BALB/c mice. The mice were infected with Porphyromonas gingivalis and Fusobacterium nucleatum 21 (early infection) or 42 days (delayed infection) after implantation. Six weeks post infection, bone volume around inserted implants was measured using micro-CT, and was compared to alveolar bone level around teeth. Histological analysis was also performed. RESULTS: The level of bone loss was significantly higher around the implants compared to the teeth, for smooth surface implants the bone loss was higher than of the SLA surface in both control and infected groups with no statistical significance. The survival rate of the implants in immediate infection was 75% compared of the 100% survival of the delayed infection and control mice. There is no significant difference between the early and the delayed infection in alveolar bone loss level around the implants. CONCLUSIONS: This model can assist in studying the differences in alveolar bone resorption in different implants and their effect on the development of the disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".