Peri‐Implant Supracrestal Tissue Characteristics Related to Abutment Materials: A Comparative Histomorphometry Study
Bibliographic record
Abstract
BACKGROUND: The peri-implant soft tissue integration is critical to maintain peri-implant health and the long-term success of dental implant rehabilitations. AIM: This comparative histomorphometry study aims to characterize the peri-implant soft tissues (PIST) around experimental abutments made of titanium (Ti), dental resin (Re), and polyetheretherketone (PEEK). METHODS: Thirty bone-level implants were placed, each receiving an experimental transmucosal healing abutment made of one of the three materials. After an 8-week healing period, the abutments and surrounding tissues were harvested and prepared for histological and histomorphometric analyses. Dimensions of sulcus depth, epithelial and connective tissue adhesion were measured. In addition, the abutment surface characteristics, levels of inflammation, plaque accumulation, and peri-implant bone level changes were evaluated. RESULTS: The dimensions of the different components of the PIST were comparable across the three experimental groups. The mean overall dimensions of the PIST were 2.68 ± 0.51 mm for Ti, 2.66 ± 0.47 mm for Re, and 2.32 ± 0.55 mm for PEEK. Mean sulcus depth was 0.71 ± 0.69 mm for Ti, 0.74 ± 0.50 mm for Re, and 0.68 ± 0.63 mm for PEEK. Mean junctional epithelium was 1.82 ± 0.67 mm for Ti, 1.56 ± 0.47 mm for Re, and 1.53 ± 0.40 mm for PEEK. Mean harvested connective tissue (until abutment platform) was 0.30 ± 0.29 mm for Ti, 0.36 ± 0.38 mm for Re, and 0.09 ± 0.10 mm for PEEK. However, the resin group exhibited significantly more supramucosal biofilm adhesion (p = 0.026). CONCLUSION: The PIST around abutments made of PEEK, resin, or titanium tend to develop in a similar pattern. However, longer observation periods are required to evaluate the long-term effects.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".