Efficacy of Cleaning Methods for the Trans‐Mucosal Parts of Zirconia Monolithic Crowns
Bibliographic record
Abstract
BACKGROUND: Dental crowns have surface pollutants after their manufacturing. We know that these pollutants can be a source of peri-implant inflammation for some cases. This study aimed to compare two dental crowns cleaning methods that are simple and quick to apply in the dental lab. OBJECTIVES: To characterize qualitatively and quantitatively the pollution of transmucosal parts of zirconia monolithic crowns after supra-mucosal glazing in the lab and to compare the efficacy of steam versus ultrasonic cleaning protocols. MATERIAL AND METHODS: Eighteen customized zirconia monolithic crowns were divided into two groups of 9 crowns receiving a different cleaning protocol. The first group was treated with steam cleaning, whereas the second group was initially rubbed with a sterile compress soaked in a detergent and then cleaned in three successive ultrasonic baths containing a detergent, sterile water, and 70% ethanol. The presence and nature of the contaminants were investigated by BSE-SEM and energy-dispersive X-ray spectroscopy microanalysis. RESULTS: Organic (e.g., paint, sweat) and inorganic (e.g., zirconia fragments, silica, and metals) were identified on the surface of the zirconia crown before the cleaning treatments. At baseline, pollutants cover 0.51% ± 0.26% of the total area. This percentage dropped, respectively, to 0.02% ± 0.03% after steam cleaning (p < 0.0001) and to 0.02% ± 0.01 after the ultrasonic cleaning protocol (p = 0.0026). No difference was observed between the two decontamination techniques (p > 0.9999), but the variance in the steam group was higher compared to the ultrasound group (p = 0.0042). CONCLUSIONS: Both protocols allowed the cleaning of the transmucosal parts of the zirconia crowns to an extent of 99.98% of the studied surface. However, the ultrasound technique displayed less variability in the removal of residual pollutants and therefore should be preferred.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".