Efficacy of combined chemical and electrochemical decontamination treatments on contaminated healing abutments and their effect on surface topography: An in vitro study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of four decontamination protocols on contaminated healing abutments (HAs) and their effects on surface topography. METHODS: Eighty contaminated single-use HA samples collected from human participants were stained with phloxine B and examined microscopically. The retrieved HAs were randomly divided into four test groups: (1) Autoclaving only (AU), (2) 5.25% sodium hypochlorite (NaOCl) + AU, (3) Electrochemical treatment (EC) + AU, (4) NaOCl + EC + AU, and positive control (contaminated without any treatment). Four new unused HAs served as negative controls (NC). The surface features were analyzed using stereo microscopy (SM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and optical profilometry. RESULTS: The lowest decontamination efficacy was observed for the AU group. The NaOCl + AU and EC + AU groups effectively removed residual contamination, whereas EC + AU showed better decontamination results than NaOCl + AU. SM, SEM, and EDS analyses revealed the best decontamination efficacy in the combined NaOCl + EC + AU group compared to the other groups. Surface roughness (Sa), developed surface area ratio (Sdr), and texture-aspect ratio (Str) in AU, NaOCl + AU, EC + AU, and NaOCl + EC + AU groups were not statistically significant compared to the NC group. CONCLUSIONS: The combination of NaOCl with subsequent EC can remove soft and hard deposits from the surface of HAs compared to NaOCl alone and EC alone, without altering the surface topography of HAs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".