Effect of denture cleansers, surface finish, and temperature on Molloplast B resilient liner color, hardness, and texture
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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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) | 0.000 | 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) | 0.002 | 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".