Retrospective assessment of patients' risk for peri‐implant diseases using the implant disease risk assessment (IDRA) tool: A cohort study
Bibliographic record
Abstract
INTRODUCTION: The implant disease risk assessment (IDRA) tool was designed to assess an individual's risk of developing peri-implant diseases by evaluating and integrating multiple risk factors. This study aimed to evaluate the IDRA tool to determine the risk of developing peri-implant disease in patients rehabilitated with dental implants. METHODS: A retrospective observational cross-sectional study was conducted, collecting data from 92 patients with 92 selected dental implants. Data included the history of periodontitis, sites with bleeding on probing (BoP), teeth and/or implants with probing depths (PDs) ≥ 5 mm, alveolar bone loss relative to the patient's age, susceptibility to periodontitis, the frequency of supportive periodontal therapy (SPT), the distance from the restorative margin (RM) of the implant-supported prosthesis to the marginal bone crest (MBC), and factors related to the prosthesis itself. Additionally, the validated instrument periodontal risk assessment (PRA) was employed for comparison. Statistical analyses utilized Chi-square, Mann-Whitney, and ROC curve. RESULTS: Outcomes indicated that 62 implants (67.4%) were classified as high-risk. Among the IDRA parameters, history of periodontitis was the primary factor contributing to an increased risk (p < 0.001). IDRA revealed high sensitivity (100%) and low specificity (63%) (AUC = 0.685; 95% CI: 0.554-0.816; p = 0.047), and there was a low agreement between the IDRA and PRA tools (Kappa = 0.123; p = 0.014). The peri-implant disease developed in 16 implants with 5.44 (±2.50) years of follow-up, however, no significant association was observed between the high- and low-medium risk groups and the occurrence of peri-implant diseases. CONCLUSION: Most of the evaluated implants presented high IDRA risk. The IDRA tool exhibited high sensitivity and low specificity; no significant association was observed between the risk profile and the development of peri-implant diseases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".