Perbedaan Kekerasan Mikro Giomer dan Kompomer setelah Prosedur In Office Bleaching menggunakan Bahan Karbamid Peroksida 45
Bibliographic record
Abstract
Teeth whitening techniques using carbamide peroxide has been approved in several major countries like USA, Canada, and Europe because it is safer, cheaper, and also effective for vital tooth whitening procedure than hydrogen peroxide. Giomer and Compomer are restorative material that combines the advantageous properties of composite resin with glass ionomer cement. The purpose of this study was to determine differences of giomer and compomer microhardness after the proceure in office bleaching using carbamid peroxide 45%. The study was conducted on 20 specimens giomer and 20 specimens compomer that have been applied using carbamid peroxide 45%. The specimens were stored in artificial saliva at a temperature 37°C for 7 days before microhardness was tested using the Vickers Hardness Tester (Buehler, Germany). Microhardness value were analyze by independence sample t test at confidence level 95% (a = 0,05). Independence sample t test results showed that there were significant differences in microhardness giomer and compomer after using bleaching agent carbamide peroxide 45% (p<0,05). The conclusion of this study is that there are differences in surface micro hardness giomer and compomer after treatment with carbamide peroxide 45%. Giomer microhardness is higher than compomer after treatment with bleaching agents carbamide peroxide 45%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".