Morphology and severity of peri‐implantitis bone defects
Bibliographic record
Abstract
BACKGROUND: Peri-implant defect morphology has shown to potentially impact upon the reconstructive outcomes for the management of peri-implantitis. Given the role that defect morphology plays upon the decision-making in the treatment of peri-implantitis, the present study aimed at assessing the morphology and severity of peri-implantitis bone defects and to insight on the patient-, implant- and site-related variables associated to these. MATERIAL AND METHODS: A cone-beam computed tomography study was carried out to classify peri-implantitis defects according to the type of defect, number of remaining bony walls and severity according to the extension of vertical bone loss. Three major defect categories were proposed: class I-infraosseous; class II-horizontal; class III-combined of class I and II. These were then subclassified into: (a) dehiscence; (b) 2/3-wall; and (c) circumferential-type defect. According to the severity the defects were further subclassified into: A: advanced; M: moderate; and S: slight. In addition, 20 site-, implant-, and patient-related variables were analyzed by generalized estimating equations (GEEs) of multilevel logistic regression models. RESULTS: Based on an a priori power calculation, 332 implants were screened in 47 peri-implantitis patients. Of these, 158 peri-implantitis implants were eligible. The most prevalent defect morphology type was class Ib (55%) followed by class Ia (16.5%), and class IIIb (13.9%). On the contrary, the less frequent defect was class II (1.9%). The most frequent degree of severity was M (50.6%) with S (10.1%) being the least prevalent. Buccal bone loss was significantly greater compared to the other bony walls in class I and class III defects. Age was associated with the type of defect. Age and smoking habit were associated with the morphology of the defects, while smoking habit, type of prosthesis and distance to adjacent implant were associated with the severity of the defects (vertical bone loss). CONCLUSION: Peri-implantitis defects frequently course with an infraosseous component and often with buccal bone loss. Certain patient-, implant-, and site-specific variables are related with defect morphology and severity. However, morphological patterns for peri-implantitis bone defects could not be proven (NCT NCT03777449).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".