Evaluation of Cleaning Methods for Lithium Disilicate Ceramic Post Try-In Paste Application: An SEM Analysis
Bibliographic record
Abstract
This in vitro study assessed the efficacy of three cleaning methods on lithium disilicate ceramic after the application of different try-in pastes through SEM analysis. Ten rectangular specimens of IPS e.max CAD were prepared using a diamond disc, crystallized, etched with 5% hydrofluoric acid, and subjected to three try-in pastes—Calibra ©, Variolink (V), RelyX Veneer®—and three cleaning techniques—air–water spray (RD), ultrasonic bath in distilled water for five minutes (ULT/W), and ultrasonic bath in distilled alcohol for five minutes (ULT/A). A control specimen was also included. After one-minute paste application and subsequent cleaning method application, SEM evaluation was conducted. The results indicate that RD was as effective as CTRL in removing remnants from R-RD, V-ULT/W and V-ULT/A samples, but ineffective for all Calibra paste-contaminated specimens. In conclusion, the optimal removal of try-in paste residues from lithium disilicate restorations is paste-dependent; however, ultrasonic baths with distilled water or alcohol proved effective for most pastes tested.
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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 | 0.004 | 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".