Dental implant proximity to adjacent teeth: A retrospective study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this study was to assess the occurrence and prognosis of dental implant proximity or direct contact with the adjacent tooth and to evaluate the symptoms and complications in both the implant and the adjacent tooth. We then elaborate on treatment modalities and discuss the prevention of this phenomenon. MATERIALS AND METHODS: This retrospective study was conducted based on the dental clinical and radiographic records of 43 patients with implant-tooth proximity of <1.0 mm or direct implant-tooth contact. Multivariate Bayesian logistic regression was performed to examine the influence of individual variables on correcting major clinical variables. RESULTS: In the multivariate regression analysis, the rate of occurrence of tooth symptom decreased by about 95% with every increase of 1.0 mm distance between implant and tooth (odds ratio [OR] = 0.1, 95% confidence interval [CI]: 0.004-0.680, p = 0.024). In the case of implant-tooth root proximity in the anterior area, the OR of peri-implantitis occurrence was 30.4-fold greater than in posterior sites (OR = 30.4, 95% CI: 1.189-785.914, p = 0.040). CONCLUSION: Implant-tooth root proximity or direct implant-tooth contact is an iatrogenic factor that causes various complications, including devitalization of the adjacent tooth and peri-implantitis. Proactive prevention with surgical stents and intra-operative periapical radiographs is needed. If proximity or contact is found during surgery, repositioning of the fixture to the correct location is recommended in order to maintain peri-implant health and prevent complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".