Long‐Term Effect of Surface Roughness and Patients' Factors on Crestal Bone Loss at Dental Implants. A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Publications from 2011 to 2015 were selected to evaluate effect of implant surface roughness on long-term bone loss as surrogate for peri-implantitis risk. 87 out of 2,566 papers reported the mean bone loss after at least 5 years of function. Estimation of the proportion of implants with bone loss above 1, 2, and 3 mm as well as analysis the effect of implant surface roughness, smoking, and history of periodontitis was performed. By means of the provided statistical information of bone loss (mean and standard deviation) the prevalence of implants with bone loss ranging from 1 to 3 mm was estimated. The bone loss was used as a surrogate parameter for "peri-implantitis" given the fact that "peri-implantitis" prevalence was not reported in most studies or when reported, the diagnostic criteria were unclear or of dubious quality. The outcome of this review suggests that peri-implant bone loss around minimally rough implant systems was statistically significant less in comparison to the moderately rough and rough implant systems. No statistically significant difference was observed between moderately rough and rough implant systems. The studies that compared implants with comparable design and different surface roughness, showed less average peri-implant bone loss around the less rough surfaces in the meta-analysis. However, due to the heterogeneity of the papers and the multifactorial cause for bone loss, the impact of surface roughness alone seems rather limited and of minimal clinical importance. Irrespective of surface topography or implant brand, the average weighted implant survival rate was 97.3% after 5 years or more of loading. If considering 3 mm bone loss after at least 5 years to represent the presence of "peri-implantitis," less than 5% of the implants were affected. The meta-analysis indicated that periodontal history and smoking habits yielded more bone loss.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".