The Influence of Patient‐, Site‐, and Implant‐Related Factors on Marginal Bone Levels of Dental Implants in a Rural Population in China: A Retrospective Study
Bibliographic record
Abstract
OBJECTIVES: Limited research is available on implant treatment outcomes in rural populations. This may be due to the presence of various barriers, such as access to oral health care, resources, health literacy, and education. The aim of this study was to evaluate the influence of patient-, site-, and implant-related factors on marginal bone levels of dental implants in a rural population in China. MATERIAL AND METHODS: A retrospective study was conducted using data from a private dental office. Subjects included in this study received dental implants as part of their routine dental treatment. Information on age, gender, smoking status, diabetes, heart disease, jaw location, restorative type, loading protocol, survival rate, implant length, and diameter was collected. Marginal bone loss was recorded as the largest value at either the mesial or distal aspect on peri-apical radiographs. Descriptive and inferential statistics were performed along with linear regression analysis. RESULTS: Overall, 428 implants were placed in 90 subjects over an average follow-up period of 453 days. No implant failures were recorded. The average marginal bone loss was 0.10 mm, with 80.6% of implants showing no marginal bone loss. The extent of marginal bone loss was greater in the mandible (0.13 ± 0.25) than in the maxilla (0.08 ± 0.19). An increase in implant diameter by 1 mm resulted in 0.08 mm of marginal bone loss, indicating wider diameter implants are associated with more bone loss. Age was also positively correlated with marginal bone loss, increasing by 0.002 mm per year. No differences were found for gender, smoking, diabetes, heart disease, restoration type, and immediate loading. CONCLUSIONS: Dental implant therapy in a rural Chinese population demonstrated high survival rates and minimal marginal bone loss. Factors such as age, implant location, and diameter influenced bone loss. This study fills a critical gap in understanding implant outcomes specifically within rural settings, highlighting the need for tailored approaches to enhance patient access and care in these communities. Further research is needed to explore these relationships and assess implant outcomes in rural populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".