Bacterial Persistence in Dentoalveolar Bone Following Extraction: A Microbiological Study and Implications for Dental Implant Treatment
Bibliographic record
Abstract
BACKGROUND: The microbiological status of apparently healed alveolar bone implant sites is unknown. Implant success may be compromised by site-specific persistence of bacterial biofilm co-aggregations contaminating healed alveolar bone. PURPOSE: The purpose of the present study was to investigate whether extraradicular infection can persist in apparently healed alveolar bone and to develop a surgical debridement strategy that favors implant osseointegration. MATERIALS AND METHODS: The study was conducted on 32 private practice patients. Seventy-seven microbiological samples were taken from 16 pre-implant extraction sockets, 56 healed post-extraction osteotomies at fixture placement, and five failed fixtures. Two of the healed osteotomy samples were healed retreatment sites. Tissue fluid and bone samples were analyzed by either anaerobic/aerobic culturing or DNA molecular techniques. All patients were treated ad modum Brånemark, with a two-stage sterile surgical procedure. A search of the medical and dental literature revealed no evidence-based or best practice recommendations for the use of debridement in implant therapy. Thus, we developed a new technique for the debridement of alveolar bone found to be contaminated by persistent biofilm or planktonic bacteria. RESULTS: The results of the microbiological analysis of 77 bone and effusion samples from 47 implant sites of the 32 patients showed that overall, 32% (n = 25) had bacteria present in the sample. In 16 pre-implant extraction sockets, 69% of samples were positive for the presence of bacteria (n = 11). Of 56 osteotomies with a minimum 3-month healing at fixture placement, 21% revealed a positive culture (n = 12). Two-stage failed fixtures had 100% positive cultures (n = 5) and it was evident from radiographs that all of these failed fixtures had the apical ends close to the former tooth root end. Based on these findings, we have developed a microbiologically based surgical debridement strategy to successfully re-treat early infective failures and to place successful two-stage fixtures. CONCLUSION: Bacteria can persist as a contaminant in apparently healed alveolar bone following extraction of teeth with apical or radicular pathosis. A new technique for surgical debridement to reduce and limit this bacterial contamination has been described.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".