Effect of different concentrations and application times of proanthocyanidin gels on dentin erosion.
Bibliographic record
Abstract
PURPOSE: To analyze the effect of different concentrations and application times of proanthocyanidin gels on dentin before an erosive challenge in order to evaluate if there is a dose-response or application time-response relationship in the use of these gels for erosion prevention. METHODS: 80 bovine root dentin blocks were randomly and equally divided into 10 groups and treated according to the two factors under study (purified grape seed proanthocyanidin gel concentration and time of application): 0.05P1: 0.05% proanthocyanidin gel during 1 minute; 0.05P5: 0.05% proanthocyanidin gel during 5 minutes; 1P1: 1% proanthocyanidin gel during 1 minute; 1P5: 1% proanthocyanidin gel during 5 minutes; 5P1: 5% proanthocyanidin gel during 1 minute; 5P5: 5% proantho-cyanidin gel during 5 minutes; 10P1: 10% proanthocyanidin gel during 1 minute; 10P5: 10% proanthocyanidin gel during 5 minutes; Control 1: placebo gel during 1 minute; and Control 5: placebo gel during 5 minutes. The gels were applied over dentin blocks once before the first erosive challenge. After that, the blocks were subjected to three erosive cycles per day, during 5 days. Profilometry was used to quantify the dentin loss (µm). Data were analyzed by two-way ANOVA and Fisher's test (P< 0.05). RESULTS: Statistical analysis showed that there was no significant difference between the application times. The different concentrations of proanthocianidin gels presented similar results (P> 0.05). All tested gels resulted in significantly less wear when compared to the placebo gel. CLINICAL SIGNIFICANCE: Grape seed proanthocyanidin gels could be considered as a promising therapy to diminish erosive dentin wear because it may interact with the exposed collagen, enhancing the demineralized organic matrix stabilization, which acts as a barrier against the diffusion of the acids from erosion.
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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) | 0.000 | 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".