Characteristics and frequency distribution of bone defect configurations in peri‐implantitis lesions—A series of 193 cases
Bibliographic record
Abstract
BACKGROUND: Knowledge on peri-implantitis bone defect characteristics and predictors is still limited. PURPOSE: To describe peri-implantitis bone defect characteristics and identify possible predictors. METHODS: Various parameters at patient- (age, gender, smoking, and supra-structure), implant- (surface, type, connection, platform, and misfit), and site level (region, alveolar ridge position, defect characteristics, neighboring structure) were recorded retrospectively. RESULTS: Among 193 implants, the most prevalent defects were class Ic (25.4%), and Id (23.8%); a previously non-described category "class Id with only one bone wall" was frequently observed (11.9%). Mean intrabony defect depth and width ranged from 4.5 to 6.2 mm and from 2.7 to 2.9 mm, respectively; mean dehiscence extent ranged from 2.8 to 7.0 mm. A total of 37.8% of the defects presented horizontal bone loss and an intrabony component; in 52.7% of the implants, total defect extent was >6 mm. Jaw region, implant position within the alveolar ridge, and implant/abutment misfit showed significant associations either to defect configuration and/or defect extent. CONCLUSION: (a) Most common peri-implantitis defects exhibited a combination of intrabony component and a buccal/oral dehiscence, while purely circumferential defects were relatively seldom; (b) implants with defects with bone dehiscence were placed more frequently closer to the lateral aspect of the ridge harboring the dehiscence; (c) implants placed in the lower anterior region had the highest risk for more severe peri-implant bone loss; and (d) peri-implant bone defects with only a single bone wall appropriate for regenerative procedure were relatively frequent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".