Experimental self-etching resin infiltrants on the treatment of simulated carious white spot lesions
Bibliographic record
Abstract
OBJECTIVES: To evaluate the penetration depth (μm) of experimental resin infiltrants containing different percentages of triethylene glycol dimethacrylate (TEGDMA) and phosphoric acid 2-hydroxyethyl methacrylate ester (PAM) in artificial carious white spot lesions (WSL). METHODS: WSL were produced in 65 bovine flat enamel specimens by pH cycling protocol, which were treated with either Icon (control) or experimental acidic infiltrants based on different percentages of TEGDMA and PAM monomers (acidic), and their association or not with previous acid-etching with phosphoric acid. Ten readings using Confocal Laser Scanning Microscopy were conducted on each specimen and the penetration depth was calculated from the surface until the deepest point with the fluorescent dye Rhodamine B (0.02 mg/mL). The pH and the viscosity of the experimental infiltrants were also tested. Data were statistically analyzed with two-way ANOVA and Tukey tests (α < 0.05). RESULTS: The material factor and the interaction material*acid-etching were statistically significant. The lowest penetration depth was observed for the samples treated with the commercial infiltrant after etching with 15% hydrochloric acid. When specimens were pre-treated with PA, highest penetration was seen for specimens treated with 100% TEGDMA, which differed from all other groups. The lowest penetration was seen for those treated with 50:50 TEGDMA:PAM infiltrants. When specimens were not previously etched, highest penetration was seen for Icon, which differed only from those treated with 25% TEGDMA 75% PAM, where the lowest values were seen. The values of viscosity increased and the pH decreased with the addition of PAM in the infiltrant formulations. CONCLUSION: the association between TEGDMA and PAM seems to allow similar infiltration depth reached by Icon infiltrant without acid etching the enamel surface.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".