Knoop hardness of five composites cured with single-peak and polywave LED curing lights.
Bibliographic record
Abstract
OBJECTIVE: As resin-modified glass-ionomer cement (RMGIC) is an adhesive material, its association to dentin bonding agents (DBAs) was previously proposed. This study investigated the adjunctive behavior of an RMGIC with etch-and-rinse bonding systems under in situ/ex vivo cariogenic challenge. METHOD AND MATERIALS: Bovine enamel blocks (3 3 3 3 2 mm) were randomly assigned to group VP, Vitremer + its own primer (3M ESPE); group VSB, Vitremer + Single Bond (3M ESPE); and group VPB, Vitremer + Prime and Bond 2.1 (Dentsply). Two blocks of each group were randomly placed in an acrylic palatal appliance, so each appliance included six blocks. Volunteers (n = 10) wore these appliances according to given instructions to promote a sucrose challenge eight times/day for 15 days. After this period, the blocks were removed from the devices and cleaned, and demineralization was assessed through longitudinal microhardness analysis (Knoop indenter, 25 g/5 s). Data were submitted to three-way ANOVA and Tukey test (P < .05). RESULTS: No treatment was able to completely avoid demineralization. All materials showed a statistically significant difference in mineral loss when the microhardness on the outer enamel was compared with deeper regions (P < .05). CONCLUSION: Association of the tested RMGICs with etch-and-rinse DBAs did not seem to be more beneficial against caries than the conventional treatment with RMGIC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".