Soft Tissue Integration of Hydroxyapatite‐Coated Abutments for Bone Conduction Implants
Bibliographic record
Abstract
PURPOSE: The protocol for bone conduction hearing implant surgery involves reduction of soft tissues around the abutment to minimize the risk of skin-related complications. The present investigation was undertaken to demonstrate that hydroxyapatite-coated abutments provide improved soft tissue integration compared with conventional (pure titanium) abutments and are suitable for use without surgical removal of subepidermal soft tissues. MATERIALS AND METHODS: Forty-eight implants for bone conduction with two different types of abutments (test and control) were inserted in the skull parietal part of eight sheep. Test abutments had a hydroxyapatite-coated surface and a concave shape. Conventional titanium abutments were used as controls. A follow-up time of 4 weeks was used. Histomorphometric analyses of test and control samples were analyzed, and morphometric results were compared using mixed model analysis. RESULTS: Histological assessment showed healthy soft tissues around the abutments with limited or no signs of inflammation. Hydroxyapatite-coated abutments showed intimate dermal adherence, while less close contact was noted for control abutments. Statistically significant differences in mean pocket depth (0.4 vs 1.6 mm, p = .0013) and epidermal downgrowth (0.6 vs 2.0 mm, p = .0003) between test and control abutments were recorded. CONCLUSION: The study confirms that hydroxyapatite-coated abutments resulted in a significant reduction in pocket depth and improved soft tissue integration compared with conventional titanium abutments, possibly by providing tight adherence at the interface. Statistically significant reduced pocket depth formation and epidermal downgrowth were recorded.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".