A 9‐Year Prospective Case Series Using Multivariate Analyses to Identify Predictors of Early and Late Peri‐Implant Bone Loss
Bibliographic record
Abstract
PURPOSE: The study aims to identify predictors of early and late peri-implant bone loss following complete implant-supported rehabilitation using multivariate analyses. MATERIALS AND METHODS: Fifty patients (28 women, 22 men; mean age 58, range 35-76) in need of a complete implant-supported rehabilitation on five to eight implants were consecutively treated. Patients were reinvited for a clinical and radiographic examination after an average 9 years of function. Implant survival and peri-implant bone loss were considered the dependent variables. Multivariate analyses were adopted to identify predictors of early and late peri-implant bone loss. RESULTS: In total, 39 patients were examinated. Two implants failed after 4 years of function, resulting in an overall survival rate of 99.2%. After a mean follow-up of 9 years, mean bone loss of 1.68 mm (SD 2.08, range -1.05 to 10.95) was found. The abutment height was a significant predictor of early peri-implant bone loss (1 year) (p = .024), whereas smoking (p = .046) and history of periodontitis (p = .046) affected late peri-implant bone loss. CONCLUSION: Within the limits of this study, it can be concluded that initial bone remodeling was affected by soft tissue thickness as reflected by the height of the abutment, whereas smoking and history of periodontitis affected long-term peri-implant bone stability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".