Retrospective cohort study of 4,591 dental implants: Analysis of risk indicators for bone loss and prevalence of peri‐implant mucositis and peri‐implantitis
Bibliographic record
Abstract
BACKGROUND: Due to the risk of peri-implantitis, following dental implant placement, this study aimed to evaluate risk indicators associated with marginal bone loss from a retrospective open cohort study of 4,591 dental implants, placed in private practice, with 5- to 10-year follow-up. Furthermore, the prevalence of mucositis and peri-implantitis among the study cohort was evaluated, comparing strict versus relaxed criteria for bleeding on probing. METHODS: Periapical radiographs were used to evaluate changes in crestal bone level. Peri-implant soft tissue was evaluated using an ordinal mucosal index in comparison with the conventional binary threshold for bleeding (i.e., present or not). Periodontal probing depth was not evaluated. Linear mixed models were used to evaluate bone level over time, and other risk indicators, at the patient and implant level. RESULTS: Risk indicators found to have a significant impact on bone level included: autoimmune disease, heavy smoking, bisphosphonate therapy, implant location, diameter and design, and the presence of a bone defect at site of implantation. The prevalence of mucositis at the implant level was 38.6% versus 14.2% at 6 to 7 years, when using strict versus relaxed criteria, respectively. The prevalence of peri-implantitis after 6 to 7 years was 4.7% and 3.6% when using strict versus relaxed criteria, respectively. CONCLUSIONS: The results of this study identify several risk factors associated with bone loss. Furthermore, the prevalence of mucositis and peri-implantitis was shown to be lower at both the implant and the patient when using strict versus relaxed criteria based on the assessment of oral health surrounding dental implants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".