Early Loading of Interforaminal Implants Immediately Installed after Extraction of Teeth Presenting Endodontic and Periodontal Lesions
Bibliographic record
Abstract
BACKGROUND: Infection in tooth extraction sites has traditionally been considered an indication to postpone implant insertion until the infection has been resolved. PURPOSE: The aim of this study was to evaluate the survival rate of early-loaded implants placed immediately after extraction of teeth with endodontic and periodontal lesions in the mandible. MATERIALS AND METHODS: Twenty patients in need of mandibular implant treatment and with teeth showing signs of infection in the interforaminal area were included in the study. The patients received four to six implants (Brånemark System, Nobel Biocare AB, Göteborg, Sweden) in or close to the fresh extraction sockets and received a provisional prosthesis within 3 days. Final prostheses were delivered after 3 to 12 months. The surgical protocol paid special attention to the preservation of high implant stability and control of the inflammatory response. The patients were followed up for 15 to 44 months. RESULTS: No implants were lost, resulting in a 100% survival rate. A mean marginal bone loss of 0.7 mm (SD 1.2 mm) was registered during the observation period. No signs of infection around the implants were detected at any follow-up visit. CONCLUSION: A high survival rate can be achieved for immediately placed and early-loaded implants in the mandible despite the presence of infection at the extracted teeth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".