Reimplantation of Dental Implants following Ligature‐Induced Peri‐Implantitis: A Pilot Study in Dogs
Bibliographic record
Abstract
OBJECTIVES: This preliminary investigation aimed to evaluate the potential of contaminated implants to reosseointegrate into pristine sites and, in addition, to assess the potential of osseointegration of new implants in peri-implantitis sockets in a canine model. METHODS: All mandibular premolars were bilaterally extracted from two mongrel dogs. Following 12 weeks of healing, two dental implants were inserted on each hemiarch. Forty-five days following implant placement, a silk ligature secured with cyanoacrylate was placed around the implants' cervical region in order to induce peri-implantitis. After another 45 days from ligature placement, the implants were mechanically removed using counter rotation with a ratchet and were reimplanted without any decontamination (neither rinsing nor chemical or mechanical cleaning) in adjacent pristine zones. In sites where implants were removed, new, wider-diameter implants were placed in the infected sockets. Forty-five days following reimplantation surgery, the dogs were sacrificed; nondecalcified specimens were processed and toluidine blue stained for morphologic and morphometric (bone-to-implant contact [BIC]) assessment under an optical microscope. RESULTS: In dog 1 all the implants (both in the pristine and in the infected sites) survived and osseointegrated while in dog 2, six out of eight implants failed to osseointegrate and exfoliated. Overall, the mean BIC of all implants was 51.08% (SD 20.54). The mean BIC for the infected implants placed into pristine sites was 51.48% ± 26.29% (SD) and the mean BIC for the new implants in peri-implantitis socket was 50.58% ± 14.27% (SD). CONCLUSIONS: Within the limitations of this preliminary investigation, especially the small number of animals, osseointegration seems to be achievable both in infected sites and around contaminated implant surfaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".