Characterization of Temporary Dental Crown Materials Prepared by Different Digital Technologies
Bibliographic record
Abstract
Temporary fixed dental restorations are an essential part of prosthodontics treatment to restore aesthetics and function as well as to protect teeth from damage until treatment completion.Various digital technologies have recently been introduced for the fabrication of temporary crowns that need to be evaluated physically and mechanically.The objective of this study was to evaluate the physical and mechanical properties of temporary crown materials fabricated using various digital fabrication techniques and 3D printing systems.Methods: Four groups of temporary dental crown materials (N=8) were prepared using conventional methods and three digital systems.Groups A, manual method, B, a digital subtractive method, C, additive method with the NextDent system, and D, additive method with the Asiga system.Surface roughness (Ra), three-point bending, and Vickers microhardness tests were performed.One-way analysis of variance (ANOVA) and Fisher's multiple tests were used to compare outcomes between the groups.Results: Group B was statistically smoother (P < .05)than other groups.The flexural strength values for groups B and C were significantly higher than groups A and D. The microhardness values for groups A, B, and C were higher than that of group D. Conclusion: Both additive and subtractive methods for manufacturing temporary crowns tend to have stronger flexural strengths and smoother surfaces than those prepared by the conventional method.Additive methods vary according to the type of printer and materials and system used.Based on the test materials and 3D printer type, subtractive temporary resin and additives from NextDent printing showed superior flexural strength.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".