Wettability of saliva substitutes across various denture base fabrication techniques
Bibliographic record
Abstract
PURPOSE: The present study evaluated the contact angles (CAs) of four denture base materials subjected to different surface treatments using deionized water and saliva substitutes. MATERIAL AND METHODS: A total of 32 rectangular specimens were manufactured using four different denture base materials: heat-cured compression molded Lucitone 199 (C), milled Lucitone 199 (M), Formlabs Denture Base RP (P), and SR Ivocap High Impact (I). The CA of the surface of the unaltered, mechanically polished, and sandblasted surface specimens was evaluated after the application of five saliva substitutes: Biotene, VEGA, Spry, Moi-Stir, Dentilube, and ionized water. Ten droplet measurements were obtained for each group, with each droplet analyzed for advancing contact angle (ACA), receding contact angle (RCA), and the contact angle hysteresis (CAH) was calculated. The data of the experiment was analyzed using 2-way ANOVA, (α = 0.05) with Tukey's test. RESULTS: CAH was demonstrated to have statistically significant differences among the denture bases (p < 0.05), with unaltered 3D printed exhibiting the largest CAH, followed by unaltered milled. The unaltered denture bases exhibited higher CAH than the polished, and there were no significant differences in CAH among the polished denture bases (p > 0.05). Sandblasting increased the ACA of the milled and conventional bases. The saliva substitutes exhibited differences in ACA, with Spry and VEGA having the highest ACA, and Biotene had the lowest CA of all the saliva substitutes evaluated. CONCLUSION: The manufacturing methods of denture bases influences the CAH, while the chemical composition of the denture base specimens does not appear to affect CAH. Sandblasting increases the ACA for the milled and conventional groups. Saliva substitutes do impact the ACA. Drawing from previous research, it is hypothesized that a 3D-printed denture base or sandblasting a milled denture base may offer greater resistance to dislodgement.
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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) | 0.001 | 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".