A Multifactorial Analysis to Identify Predictors of Implant Failure and Peri‐Implant Bone Loss
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for failure and bone loss of implants in a large study sample on the basis of multivariate analyses. MATERIALS AND METHODS: Patient files of all patients referred for implant treatment from November 2004 to December 2007 were scrutinized, and information on implant- and patient-related factors was collected. The study sample in this retrospective cohort study consisted of both partially dentate and fully edentulous patients referred for various indications. The only inclusion criterion was a follow-up of at least 2 years. Implant survival and bone loss were assessed by an external investigator (SV) comparing digital periapical radiographs taken during recall visits with the postoperative ones. Univariate and multivariate tests were adopted to identify possible risk indicators for implant failure and peri-implant bone loss. RESULTS: Twenty-one of 1,320 (1.6%) implants were lost in 19 of 376 (5.1%) patients (210 female, 166 male; mean age 56, range 17-82) after a mean follow-up of 32 months (range 24-62). Based on multivariate analysis, only smoking (p = .001) and recall compliance (p = .010) had a significant influence on implant failure, with smokers more prone to failure. The overall mean bone loss was 0.36 mm (SD 0.68, range 0.00-7.10). Smoking (p = .001) and jaw of treatment (p = .001) affected peri-implant bone loss. More peri-implant bone loss was observed in smokers and in the maxilla. A clear discrepancy was found between univariate and multivariate analysis with regard to identification of risk factors. CONCLUSION: Multivariate analysis demonstrated that implant-related factors did not affect the clinical outcome, but smoking was identified as a predictor for implant failure. Predictors for peri-implant bone loss were smoking and jaw of treatment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".