Soft Tissue Attachment to Titanium Implants Coated with Growth Factors
Bibliographic record
Abstract
BACKGROUND: Enhancing the connective tissue seal around dental implants may be an important factor in implant survival. PURPOSE: The objective of the study was to investigate the effect of implant surface modification with either platelet-derived growth factor (PDGF) or enamel matrix derivative (EMD) on connective tissue attachment to titanium implants. MATERIALS AND METHODS: Eighteen implants (Branemark® Mk III Groovy NP (3.3 mmØ × 10 mm, Nobel Biocare) were implanted subcutaneously into 12 rats. Six implants each were coated with either PDGF or EMD immediately prior to implantation and six implants were left uncoated. Implants were retrieved at 4 and 8 weeks and assessed histologically to compare the soft tissue adaptation to the implant surfaces. RESULTS: Ingrowth by soft connective tissue into the threads of all implants was noted at 4 and 8 weeks. Coating with growth factors did not alter the orientation of fibroblasts and collagen fibers. The depth of connective tissue penetration into the implant grooves was significantly greater for the implants coated with PDGF at 4 weeks. The thickness of the connective tissue in growth was significantly less for the implants coated with PDGF at 8 weeks. CONCLUSION: Coating of the implant surface with rhPDGF-BB or EMD can increase the speed and quantity of soft tissue healing around the implant surface.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".