Effect of posterior implant restorations on adjacent teeth and tissues: A case–control study
Bibliographic record
Abstract
INTRODUCTION: Dental implants are an artificial substitute for extracted or missing teeth in the oral cavity and are valuable for improving dental health and quality of life. While many studies on implants can be found, few studies examine their effects on adjacent teeth and tissues. The study aimed to examine complications of teeth adjacent to dental implants in the posterior region. METHODS: In this retrospective case-control study, clinical data of patients treated with implants in the posterior segment were extracted from the medical records in a single community dental clinic between January 9, 2010 and January 9, 2020. The patients were examined clinically and radiographically every 6 months. Data on the adjacent teeth to the dental implants were collected and divided into two groups, complications ("study group") versus no-complications ("control group"). Multivariate logistic regression analysis was performed to find a possible correlation between the complications and the variables checked, followed by checking specific variables in the complication group. RESULTS: A total of 1072 patients were included in the study. There were 179 patients (16.7%) with complications in adjacent teeth, while 893 patients had no documented complications. Predisposing factors for secondary caries were smoking (OR = 2.2, CI = 1.3-3.8) and a higher number of implants (OR = 1.6, CI = 1.1-2.5). Tooth crack and tooth fracture were analyzed together and found to be related to osteoporosis (OR = 8.9, CI = 2.9-27.6), whereas males were more prone to teeth fracture (OR = 2.8, CI = 1.1-7.4). Tooth mobility was related to a higher number of implants (OR = 16.5, CI = 3.7-73.8). Further analyzing the complication group solely, there was a statistical significance for age in primary caries and tooth mobility (p = 0.045). In addition, a higher number of implants was more prevalent with tooth mobility (p = 0.002), wider implant platform was more significant with primary caries (p = 0.012), and periodontal Stage III was more prone to tooth mobility (p < 0.001). The distance between the implant and adjacent tooth was also statistically significant-close proximity with tooth mobility and high distance with dental caries (p = 0.04). CONCLUSIONS: We found a relatively high rate of complications in teeth adjacent to dental implants. Secondary caries was the most common complication. Good understanding and proper position of the implants is essential to avoid adjacent teeth 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".