The prevalence and associated factors of proximal contact loss between implant restoration and adjacent tooth after function: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Dental implant is widely used as a treatment for missing teeth. However, proximal contact loss (PCL) between implant-supported fixed dental prostheses (FDP) and adjacent teeth has been reported as one of the common and adverse complications. PURPOSE: We aimed to evaluate the prevalence of PCL up to 18 years after implant prosthesis delivery and to analyze associated factors. MATERIALS AND METHODS: A total of 317 patients who had received implant FDP at the posterior regions were included in this study. Nineteen factors were assessed, including degrees of proximal contact tightness, oral hygiene, periodontal conditions, and food impaction. Chi-square test, univariate generalized estimating equation (GEE), and multivariate GEE were utilized to identify factors influencing PCL. RESULTS: Proximal contacts at both the mesial and distal (if present) sides were evaluated. The mesial contact loss rate (27%) was significantly higher than that of the distal contact loss (5%). Increased PCL rates over functional time were observed at both the mesial and distal sides. Six factors, including patient age, implant functional years, frequent use of interdental brushes, splinting or single implant, plunger cusp, and food impaction, were revealed to be associated with the mesial PCL using the chi-square test and univariate GEE analysis. However, only functional years (>5 years), frequent use of interdental brushes and food impaction showed significance in the multivariate GEE. CONCLUSIONS: Mesial PCL was frequent and increased over functional years. An occlusal retainer and routine follow-up may help prevent PCL. Although oral hygiene conditions contribute little to PCL, food impaction and frequent use of interdental brushes were influential factors.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".