Volumetric tissue changes following combined surgical therapy of peri‐implantitis: A 2‐year follow‐up analysis. A prospective case series
Bibliographic record
Abstract
AIM: To assess volumetric tissue changes following combined surgical therapy of peri-implantitis over a follow-up period of 24 months. MATERIALS AND METHODS: A total of 20 patients (n = 28 implants) were diagnosed with peri-implantitis and underwent access flap surgery, implantoplasty, and augmentative therapy at intrabony components (ie, combined therapy) using a natural bone mineral and a native collagen membrane. The peri-implant region of interest (ROI) was intraorally scanned pre-operatively (S0), and after 12 (S3) and 24 (S4) months. Digital files were superimposed for the assessment of volumetric changes between the referred time points. The change in thickness was assessed at a standardized ROI, segmented into two equidistant sections (ie, marginal and apical). RESULTS: Peri-implant tissues exhibited a nonsignificant mean thickness loss of 0.16 (95% CI: -4 to 0.06) and 0.17 mm (95% CI: -0.05 to 0.4) at S3 and S4, respectively. S0-S3 dimensional thickness changes at marginal and apical areas were -0.24 (95% CI: -0.48 to 0.002) and -0.19 mm (95% CI: -0.36 to -0.2), respectively. Dimensional changes from S0 to S4 amounted to -0.22 mm (95% CI: -0.46 to 0.02) and -0.07 mm (95% CI: -0.09 to 0.2), respectively. The thickness changes at marginal and apical ROIs were significant from S0 to S3. Clinical parameters (ie, plaque index, bleeding on probing, and probing depth) significantly improved over the 24-month follow-up period. Linear regression analyses revealed no significant association between baseline bone loss (%), width of keratinized mucosa, and mucosal recession scores and thickness changes. CONCLUSIONS: Peri-implant tissues revealed minor volumetric changes at 12 and 24 months after combined surgical therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".