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 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 | 0.000 |
| 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".