Corrosion resistance of coupled sandblasted, large‐grit, acid‐etched (SLA) and anodized Ti implant surfaces in synthetic saliva
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate the corrosion resistance of galvanically coupled SLA and anodized implant surfaces with a Co-Cr alloy. Materials and Methods: Three groups were included in this study. The first (SLA) was composed of SLA implants (Institut Straumann, Basel, Switzerland), the second (ANO) of NobelReplace® (Nobel Biocare, Göteborg, Sweden), and the third (MIX) of both implant systems combined. All groups were assembled with a single Co-Cr superstructure. Electrochemical testing included open-circuit potential, electrochemical impedance spectroscopy, cyclic potentiodynamic polarization, and chronoamperometric current-time measurements. The quantitative results (EOCP, ECORR, ICORR, EPROT, RP, and ICA) were statistically analyzed by one-way ANOVA and Tukey's post-hoc multiple comparison test (α = 0.05). Results: All the aforementioned parameters showed statistically significant differences apart from ECORR and EPROT. The evaluation of qualitative and quantitative results showed that although SLA had higher corrosion resistance compared with ANO, it had less resistance to pitting corrosion. This means that SLA showed increased resistance to uniform corrosion but less resistance if pitting corrosion was initiated. In all cases, MIX showed intermediate behavior. Conclusion: The corrosion resistance of implant-retained superstructures is dependent on the electrochemical properties of the implants involved, and thus different degrees of intraoral corrosion resistance among different implant systems are anticipated.
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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) | 0.000 | 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".