Impact of Implant Mesiodistal Distance on Peri‐Implant Bone Loss: A Cross‐Sectional Retrospective Study
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to verify the effect of our previously proposed mesiodistal distance for multiple posterior implants on preserving peri-implant bone, and to provide a measurable criterion for predicting implant prognosis. METHODS: One hundred and two patients with dental implants placed in the posterior free-end edentulous arches were recruited in this cross-sectional study. Calibrated X-rays (peri-apical [PA] or bite-wing x-ray) were collected to measure the mesiodistal space as well as the corresponding bone resorption of implants after prosthesis placement. Implants were assigned to the test/control group according to whether their mesiodistal distance following our proposed algorithm. After adjusting covariates, logistic multivariate regression analyses were performed to examine the relationship between inter-implant distance and marginal bone loss (MBL) during each follow-up interval. The effect of smoking habits was also analyzed. RESULTS: Every observation period exhibited great significance between experimental and control group on peri-implant bone level (p = 0.006, 0.005, 0.001, 0.025, 0.001, correspondingly) and the difference had a tendency to grow as time went by except 4-year (MD = -0.19, -0.39, -0.43, -0.30, -1.26, correspondingly). While no significant difference was observed between smokers and nonsmokers in the same group (p > 0.05). CONCLUSION: The mesiodistal algorithm of 4-4.6 mm (implant to adjacent canine tooth), 7-7.4 mm, 8-8.5 mm, and 9-9.5 mm was proved to be effective for maintaining peri-implant bone level. It was also observed in our study that the impact of inter-implant distance outweighed that of smoking. This study provided clinicians predictable prognostic outcomes for implants and reference for deciding treatment plans.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".