Effect of denture cleansers, surface finish, and temperature on Molloplast B resilient liner color, hardness, and texture
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare color, texture, and Shore A hardness of a resilient silicone denture liner with as-polymerized, roughened, or pumiced surfaces after treatment with perborate-, persulfate-, or hypochlorite-containing denture cleansers at 25 degrees or 55 degrees. MATERIALS AND METHODS: Fifty-eight specimens that each exhibited an as-polymerized, a roughened, and a pumiced area were exposed to 5 different commercially available perborate-, persulfate-, or hypochlorite-containing denture cleansers at 25 degrees or 55 degrees continuously for 4 (1/2) months. The solutions were replaced twice a day. Control specimens were soaked in water with no cleanser. Before and after the 4 (1/2) -month cleaning regimen, the color, hardness, and texture of resilient liner surfaces were evaluated using a color densitometer, a Shore A durometer (Shore Instrument & Mfg Co, Freeport, NY), and a surface profilometer, respectively. Differences among groups after the cleanser treatment were determined using a repeated measures analysis of variance (alpha = 0.05) and a Tukey's Honestly Significant Difference post hoc test. RESULTS: Roughened specimen surfaces after 25 degrees or 55 degrees cleanser treatment exhibited significant color loss with some perborate-containing cleansers compared with the control. Roughened specimens treated at 55 degrees with perborate-containing cleansers also exhibited significantly greater color loss than those treated with the persulfate-containing cleanser. With roughened surfaces, significantly greater hardness was found with some perborate-containing cleanser compared with a hypochlorite-containing cleanser after treatment at 25 degrees. No differences were observed in surface texture based upon cleanser treatment. CONCLUSION: After silicone resilient denture liner treatment with certain perborate-containing denture cleansers, a greater amount of components could leach from the liner leading to a loss of color if the liner surface is rough.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it