Accuracy of bone‐level assessments following reconstructive surgical treatment of experimental peri‐implantitis
Bibliographic record
Abstract
AIMS: The purpose of this study was to evaluate the accuracy of bone-level assessments using either cone-beam computed tomography (CBCT), intra-oral peri-apical (PA) radiographs or histology following reconstructive treatment of experimental peri-implantitis. MATERIALS AND METHODS: Six Labrador dogs were used. Experimental peri-implantitis was induced 3 months after implant placement. Surgical treatment of peri-implantitis was performed and peri-implant defects were allocated to one of four treatment categories; no augmentation, bone graft materials with or without a barrier membrane. Six months later, intra-oral PA radiographs and block biopsies from all implants sites were obtained. Marginal bone levels (MBLs) were measured using PA radiographs, CBCT and histology. RESULTS: Significant correlations of MBL assessments were observed between the three methods. The measurements in PA radiographs consistently resulted in an overestimation of the bone level of about 0.3-0.4 mm. The agreement between the methods was not influenced by the use of bone substitute materials in the management of the osseous defects. CONCLUSIONS: Although MBL assessments obtained from PA radiographs showed an overestimation compared to MBL assessments on corresponding CBCT images and histological sections, PA radiographs can be considered a reliable technique for peri-implant bone-level evaluations following reconstructive surgical therapy of experimental peri-implantitis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".