Three‐Dimensional Hard and Soft Tissue Healing at Implants With A Modified Neck Design: An In Vivo Preclinical Study
Bibliographic record
Abstract
ABSTRACT Objectives To evaluate the three‐dimensional hard and soft tissue healing around a modified dental implant presenting a tissue level connection and a convergent transmucosal neck (test), compared with a conventional bone level implant (control). Material and Methods Sixteen test and 16 control implants were placed in 8 experimental animals, following a random allocation sequence. Peri‐implant bone volume and surface, 360° bone to implant contact (360‐BIC), peri‐implant soft tissue volume, and buccal and lingual peri‐implant soft tissue thickness, were evaluated through Micro‐CT and digital volumetric analysis at 4 and 12 weeks of healing. Results In the most coronal mm of the implant surface apical to the implant platform, both at 4 and 12 weeks, 360‐BIC was higher at test implants. At 4 weeks, it was approximately 4 times higher (+303.26%; ∆ = 39.83%; p = 0.05) and at 12 weeks, 2.50 times higher (+151.8%; ∆ = 43.23%; p = 0.03). In the same area of interest, a statistically non‐significant trend towards a higher 360° peri‐implant bone volume was also observed in the test group, both at 4 weeks (+ 272.72%; ∆ = 8.70 mm3; p = 0.07) and 12 weeks (+ 154.54%; ∆ = 8.32 mm3; p = 0.09), being statistically significant only for the lingual bone volume at 4 weeks (+246.47%; ∆ = 5.94 mm3; p = 0.02). No significant differences were observed when comparing the soft tissue volumes and thicknesses among the two groups. Conclusions Test implants demonstrate superior 360‐BIC and bone volumes around the most coronal implant surface, below the implant platform, while no relevant differences in soft tissue volumes and thickness were observed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".