Development and internal validation of a clinical prediction model for peri-implantitis in a sample of Canadian university-based population
Bibliographic record
Abstract
Background:Peri-implantitis represents a growing challenge in implant dentistry, with prevalence reaching 20-56% of cases after 5-10 years. While known risk factors include poor oral hygiene, smoking, and history of periodontitis, current risk assessment remains largely subjective. To enable objective, individualized risk stratification, this study aimed to develop and internally validate a predictive model for peri-implantitis risk by integrating patient-related and implant-related variables. Methods: This retrospective cohort study analyzed patients who received dental implant treatment at University of Toronto dental clinics between 2015 and 2016. The cohort comprised 553 patients (1,043 implants) who had at least one implant in function for 1 year or more until the year 2023. Peri-implantitis was diagnosed according to the 2017 World Workshop criteria. Several predictor variables were evaluated, including patient-related variables (age, sex, diabetes, smoking status, oral hygiene, and history of periodontitis) and implant-related variables (brand, site, implant restoration type). A multivariable logistic regression model was developed to predict peri-implantitis risk, with internal validation performed using 1,000 bootstrap resamples. Discrimination was assessed via the Area Under the Curve (AUC), and calibration was evaluated using the calibration slope and calibration-in the-large, and performance was evaluated using Brier score. Results: Peri-implantitis prevalence at patient-level was 13.9% in our cohort. Key peri-implantitis predictors included posterior mandibular implants, full-arch restorations, smoking, fair oral hygiene, and a history of periodontitis. The prediction model after internal validation (bootstrapping) demonstrated moderate predictive performance (AUC = 0.80, 95% CI: 0.76-0.84), with moderate calibration (slope = 0.98, 95% CI: 0.90-1.06; Calibration-in-the-large = 0.58, 95% CI: -0.42-1.58) and moderate performance (Brier score = 0.14, 95% CI: 0.11-0.17). Conclusions: This study introduces a multifactorial, internally validated model for peri-implantitis risk prediction with good discrimination and performance. The identification of modifiable risk factors offers practical opportunities for prevention and individualized care. While the model demonstrates internal validity, external validation in diverse clinical settings is necessary to confirm its generalizability.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".