Effect of argon plasma abutment activation on soft tissue healing: <scp>RCT</scp> with histological assessment
Bibliographic record
Abstract
OBJECTIVE: To assess the peri-implant soft tissue profiles between argon plasma treatment (PT) and non-treated (NPT) healing abutments by comparing clinical and histological parameters 2 months following abutment placement. MATERIALS AND METHODS: Thirty participants were randomly assigned to argon-plasma treatment abutments group (PT) or non-treated abutments (NPT) group. Two months after healing abutment placement, soft peri-implant tissues and abutment were harvested, and histological and clinical parameters including plaque index, bleeding on probing, and keratinized mucosa diameter (KM) were assessed. Specialized stainings (hematoxylin-eosin and picrocirious red) coupled with immunohistochemistry (vimentin, collagen, and CK10) were performed to assess soft tissue inflammation and healing, and the collagen content keratinization. In addition to standard statistical methods, machine learning algorithms were applied for advanced soft tissue profiling between the test and control groups. RESULTS: PT group showed lower plaque accumulation and inflammation grade (6.71% vs. 13.25%, respectively; p-value 0.02), and more advanced connective tissue healing and integration compared to NPT (31.77% vs. 23.3%, respectively; p = 0.009). In the control group, more expressed keratinization was found compared to the PT group, showing significantly higher CK10 (>47.5%). No differences in KM were found between the groups. SIGNIFICANCE: PT seems to be a promising protocol for guided peri-implant soft tissue morphogenesis reducing plaque accumulation and inflammation, and stimulating collagen and soft tissue but without effects on epithelial tissues and keratinization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".