A nomogram prediction of peri‐implantitis in treated severe periodontitis patients: A 1–5‐year prospective cohort study
Bibliographic record
Abstract
Abstract Background No nomogram of peri‐implantitis was reported before which is valuable for risk‐estimating, clinical decision‐making, and better‐patients‐communicating. Purpose To identify the risk indicators and develop a nomogram prediction model of peri‐implantitis in treated severe periodontitis patients. Materials and Methods A prospective study was conducted on 100 patients with 214 implants. Periodontal and peri‐implant parameters were evaluated at implant surgery procedure (T1), and at follow‐up (T2). Risk factors were analyzed by logistic regression analyses with generalized estimating equations. Nomogram was developed and the discriminatory ability of the model was analyzed. Results The incidence of peri‐implantitis at patient‐level and implant level were 16% and 11.2% respectively, with no implant lost. The variables associated with peri‐implantitis were the PDT1 ≥ 6 mm (%) > 10%, the implant position, length, and diameter after adjusting for covariates. A nomogram prediction model of peri‐implantitis were developed with factors of PD T1 ≥ 6 mm (%) > 10% and implant placed in posterior. The area under the ROC curves of stepwise model was 0.794. Conclusions The residual pockets and implants position were identified as predictors for the peri‐implantitis. The nomogram can be used to estimate the risk of peri‐implantitis in treated severe periodontitis patients.
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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 | 0.005 |
| 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 | 0.001 | 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".