The effect of polishing on surface roughness of tissue conditioners.
Bibliographic record
Abstract
PURPOSE: Surface roughness can affect microbial colonization of long-term denture liners, alloys, and denture acrylic. The purpose of the present study was to examine the effect of finishing and polishing procedures on surface roughness of 4 temporary resilient denture liners (tissue conditioners). MATERIALS AND METHODS: Mean surface roughness was measured for 4 materials (Lynal, Visco-gel, Coe-Soft, and Functional Impression Tissue Toner [FITT]) finished in 4 ways: unfinished (control); polished; reduced, unpolished; and reduced, polished. Samples were allowed to polymerize at 37 degrees C for 24 hours, and the surface roughness was measured using a Mitutoyo Surftest 212. RESULTS: Mean surface roughness ranged from 1.8 +/- 0.8 microns for polished Lynal to 7.8 +/- 1.1 microns for reduced, unpolished FITT. All polished samples were smoother than unpolished samples (including controls), whether or not the samples were reduced with a bur. CONCLUSION: Polished samples of tissue conditioning material had lower mean surface roughness measurements than control or reduced, unpolished samples at the 95% level of confidence. There was no difference in mean surface roughness measurements of control samples and unpolished samples reduced with a bur at the 95% level of confidence. Mean surface roughness differed significantly between the materials tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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".