In situ effect of a proanthocyanidin mouthrinse on dentin subjected to erosion
Bibliographic record
Abstract
BACKGROUND: Proanthocyanidin has been shown to be efficient in inhibiting matrix metalloproteinases. OBJECTIVE: The aim of this in situ study was to evaluate the protective effect of Proanthocyanidin-based mouthrinses either with naturally acidic or with a neutral pH applied on dentin subjected to erosion. METHODOLOGY: Eight volunteers wore one palatal device in two phases (7 days washout) with 16 samples per group (n=8). The groups under study were: First Phase/ G1 - 10% proanthocyanidin mouthrinse (pH 7.0, Experimental group 1 - Purified Grape Seeds Oligomeric Proanthocyanidins), G2 - 10% proanthocyanidin mouthrinse (pH 3.0, Experimental group 2 - Purified Grape Seeds Oligomeric Proanthocyanidins). Second Phase/ G3 - 0.12% chlorhexidine mouthrinse (pH 7.0, Positive control group), G4 - no previous treatment (Negative control group). Each device was subjected to 3 erosive cycles (5 minutes) per day for 5 days. Treatments with different mouthrinses were applied once after the second erosive challenge (5 minutes). Profilometry was used to quantify dentin loss (µm). RESULTS: Data were analyzed by repeated measures of ANOVA followed by Fisher's test (p<0.05). G1 (1.17±0.69) and G3 (1.22±0.25) showed significantly lower wear values with no statistical difference between them. G2 (2.99±1.15) and G4 (2.29±1.13) presented higher wear values with no significant differences between them. CONCLUSION: The 10% proanthocyanidin mouthrinse (pH 7.0) could be a good strategy to reduce dentin wear progression.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".