Immunohistochemical assessment of the peri‐implant soft tissue around different abutment materials: A human study
Bibliographic record
Abstract
BACKGROUND: Soft tissue reaction to dental implant abutments influences the sustainability of restoration. Several abutment materials, such as titanium and gold alloy, have been introduced for strength and esthetic solutions. Zirconia and titanium-base abutments have also become popular in recent years due to high esthetic demands. PURPOSE: To investigate the immune cell infiltration into the soft tissue in response to different abutment materials. MATERIALS AND METHODS: Twenty dental implants were placed in the posterior regions of 17 patients. Titanium, zirconia, gold alloy, and titanium-base were randomly selected for each patient on the day of surgery. After 8 weeks of healing, the abutments were removed along with 1 mm of peri-implant soft tissue. Immunohistochemical assessment was performed using labeled streptavidin-biotin to identify T cells, B cells, macrophages, plasma cells, and microvascular infiltration. RESULTS: cells (P value <.05). The number of plasma cell and new microvascular infiltrations among abutment materials was not significantly different. CONCLUSION: Titanium, titanium-base, and zirconia abutments showed comparable infiltration profiles; gold alloy abutments showed the highest B-cell, T cell, and macrophage infiltration. None of the abutment materials caused clinical inflammation; hence, they can be effectively used.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".