Implant Stability during Initiation and Resolution of Experimental Periimplantitis: An Experimental Study in the Dog
Bibliographic record
Abstract
BACKGROUND: Histologic studies have demonstrated the possibility to reestablish direct bone-implant contacts after ligature-induced periimplantitis. The influence of the reosseointegration on the stability of implants is not known. PURPOSE: The aim of the present investigation was to study bone tissue and associated implant stability alterations that occurred during induction and resolution of periimplantitis using resonance frequency analysis (RFA), radiography, and histology. MATERIALS AND METHODS: Three implants with smooth (turned) or roughened (SLA) surfaces were placed in each side of the edentulous mandible of four dogs. Experimental periimplantitis was induced for 3 months. Five weeks later, the animals were treated with antibiotics and surgical therapy and were followed for another 6 months. Periapical radiographs and RFA were used to evaluate marginal bone levels and implant stability throughout the study period. After termination, the tissue-implant interface was evaluated by light microscopy in ground sections. RESULTS: There was a linear relationship between radiographic and RFA findings because continuous loss of marginal bone and a decrease in implant stability were observed for both implant surfaces during the periimplantitis period. Antibiotic treatment and surgical therapy resulted in some reosseointegration, which was more marked for the SLA surface. The resonance frequency values corresponded well to the histometric measurements because reosseointegration resulted in an increase in implant stability. CONCLUSIONS: The findings from the present study indicate a linear relationship between marginal bone level and resonance frequency value. It is suggested that the RFA technique is sensitive and may be used to detect even a minor change in the level of bone-implant contact.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".