Defect angle as prognostic indicator in the reconstructive therapy of peri‐implantitis
Bibliographic record
Abstract
OBJECTIVE: To analyze the influence of the characteristics of bone defects caused by peri-implantitis on the clinical resolution and radiographic bone gain following reconstructive surgery. METHODS: This is a secondary analysis of a randomized clinical trial. Periapical x-rays of bone defects, caused by peri-implantitis exhibiting intrabony component, were analyzed at baseline and 12-month follow-up after reconstructive surgery. Therapy consisted of anti-infective therapy along with a mixture of allografts with or without a collagen barrier membrane. The association of defect configuration, defect angle (DA), defect width (DW), and baseline marginal bone level (MBL) with clinical resolution (based on a prior defined composite criteria) and radiographic bone gain was correlated by means of generalized estimating equations. RESULTS: Overall, 33 patients with a total of 48 implants exhibiting peri-implantitis were included. None of the evaluated variables yielded statistical significance with disease resolution. Defect configuration demonstrated statistical significance when compared to class 1B and 3B, favoring radiographic bone gain for the former (p = 0.005). DW and MBL did not demonstrate statistical significance with radiographic bone gain. On the contrary, DA exhibited strong statistical significance with bone gain (p < 0.001) in the simple and multiple logistic regression analyses. Mean DA reported in this study was 40°, and this resulted in 1.85 mm radiographic bone gain. To achieve ≥1 mm of bone gain, DA must be <57°, while to attain ≥2 mm of bone gain, DA must be <30°. CONCLUSION: Baseline DA of peri-implantitis intrabony components predicts radiographic bone gain in reconstructive therapy (NCT05282667-this clinical trial was not registered prior to participant recruitment and randomization).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".