The relationship of <scp>peri‐implant</scp> soft tissue wound healing with implant cover screw design: Cross‐sectional study
Bibliographic record
Abstract
INTRODUCTION: Dental implants are frequently preferred method for oral rehabilitation all over the world. The incidence of various complications such as incorrect prosthesis, peri-implant mucositis, and peri-implantitis is high; premature loss of implants is encountered due to osteointegration process not being completed for some unexplained reasons. However, there is no study in the literature examining the nonfunctional period of implants. Closure screws of different implant companies have different designs like surface properties, and areas, where closure screws sit, are important reservoirs for microorganism colonization. Our study aims to evaluate the inflammatory response, epithelial maturation, and epithelial-connective tissue interaction around closure screws. METHODS: For this purpose, 52 implants belonging to five different implant companies were included in the study. Tissues removed over the cover screw during fitting of healing caps were used as biopsy material and for epithelial proliferation Ki-67, for epithelium-connective tissue interaction Syndecan-1, and for macrophage activation CD-68 expressions were evaluated by immunohistochemical analysis. Scanning electron microscopy (SEM) analyzes were performed to evaluate the presence of gap between the implant and the cover screw. RESULTS: As a result of our study, intensity of subepithelial inflammation between groups wasn't statistically different. Differences in CD-68 and Syndecan-1 levels were obtained at the lamina propria level. H score of CD-68 was statistically significantly different in epithelium (p = 0.032), and H score of Syndecan-1 was different in lamina propria (p = 0.022). There wasn't a statistically significant difference between the groups for Ki-67 (p = 0.151). CONCLUSION: Our study results indicate that in addition to the implant surface morphology, the design of the closure screws is important in the inflammatory response and epithelial maturation that develops during wound healing. Although the inflammatory response is required for healing, osteointegration, and implant survival, further investigation is needed to investigate the relationship between initial neck resorption and closure screws with radiographic and microbiological examinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".