An In Vitro Study to Assess the Effect of Cigarette Smoke on Color Stability and Surface Roughness of 3D Printed, Milled, and Traditional Provisional Crown and Bridge Materials
Bibliographic record
Abstract
Objectives To assess the influence of cigarette smoke (CS) on the color and surface roughness of 3D printed, milled, and traditionally fabricated provisional crown and bridge (PC&B) materials. Materials and methods 112 disc-shaped samples were made employing four techniques and materials (28 per group) to fabricate PC&B prostheses. Specimens were fabricated using standard protocols, such as 3D printing, milling, conventional bis-acrylic resin, and traditional autopolymerizing polymethyl methacrylate (PMMA) resin. After preliminary color and surface roughness recording, each group specimen was divided randomly into two subgroups (14 each). The artificial saliva acted as the storage media for the control group specimens for 30 days, and test group specimens were subjected to CS in a customized smoking chamber (10 minutes twice daily, for 30 minutes). Final color and surface roughness measurements were made. The change in color (∆E00) and surface roughness (∆Sa) were calculated, and the data was tabulated for analysis. Statistical analysis One-way analysis of variance was used to analyze the change in color and surface roughness. Post-hoc Tukey HSD test was used for comparison between groups. Results The mean ΔE00 and ∆Sa were higher among groups exposed to CS than those exposed to artificial saliva. The maximum change in color was recorded in the autopolymerizing PMMA, whereas the 3D printed resins recorded the minimal change. The traditional bis-acrylic resin recorded the maximum mean surface roughness change, while the milled resin recorded the least change. Conclusions Within the study limitations, it can be inferred that when exposed to CS, 3D printed and milled PC&B materials have superior color stability and displayed less change in surface roughness when equated with traditional bis-acrylic and autopolymerizing PMMA resins.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".