Surface characterization of titanium implant healing abutments before and after placement
Bibliographic record
Abstract
BACKGROUND: Implant healing abutments (IHA) have a vital role in soft tissue healing after implant placement. Although there is thorough investigation on the implant surface, little is known about the effects potentially damaging oral conditions impose on healing abutments. PURPOSE: To characterize the surface of titanium healing abutments before and after clinical placement to understand the effects of the oral environment and time on the device surface. MATERIALS AND METHODS: Ten regular Straumann IHA were subjected to characterization pre and postplacement to elucidate the effects of the oral environment on device surfaces. Changes in surface crystallinity, morphology, and elemental composition were monitored with Raman spectroscopy, scanning electron microscopy, optical microscopy, and x-ray photoelectron spectroscopy, respectively. In addition, corrosion rate and polarization resistance were obtained to assess electrochemical device stability after placement. RESULTS: Control analysis indicated the titanium oxide of IHAs was thicker than natural commercially pure titanium and had the structure of crystalline anatase. After removal, the abutments possessed large amounts of biological debris, visible scratches, and discoloration sparsely on the surface. Spectroscopic analysis revealed the titanium oxide on the surface of IHAs was structurally unchanged, with crystalline titanium dioxide still present on the surface. Electrochemical results revealed that implanted healing abutments possessed a significantly higher corrosion rate than controls (change in corrosion rate = 2.34 ± 0.58 nm/year). CONCLUSIONS: Healing abutments were stable in the oral environment due to the chemical stability of the oxide, and were likely subjected to abrasions from unintentional loading and oral hygiene techniques.
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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 | 0.001 | 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".