Six-year clinical performance of lithium disilicate glass-ceramic CAD-CAM versus metal-ceramic crowns
Bibliographic record
Abstract
PURPOSE: To assess the clinical performance of monolithic CAD-CAM lithium disilicate glass-ceramic (LDGC) crowns and metal-ceramic (MC) crowns provided by predoctoral students. This study also assessed the effects of patient and provider-related factors on their clinical performance as well as patient preference for these types of crowns. MATERIALS AND METHODS: Twenty-five patients who received 50 crowns (25 LDGC CAD-CAM and 25 MC) provided by predoctoral students were retrospectively examined. LDGC CAD-CAM crowns were milled in-house using the CEREC Bluecam system and cemented with either RelyX Unicem or Calibra Esthetic resin cements. MC crowns were cemented with RelyX Unicem cement. Clinical assessment of the crowns and the supporting periodontal structures were performed following the modified California Dental Association (CDA) criteria. Patients' preference was recorded using a visual analog scale (VAS). The results were statistically analyzed using log-rank test, Pearson Chi-squared test and Kaplan-Meier survival analysis. RESULTS: Twelve complications were observed in the MC crown group (9-esthetic, 2-technical and 1-biological). In comparison, 2 complications in the LDGC CAD-CAM crown group were observed (1-technical and 1-esthetic). The 6-year cumulative survival rates for MC crowns and LDGC CAD-CAM were 90.8% and 96%, respectively, whereas the success rates were 83.4% and 96%, respectively. Overall, patients preferred the esthetic outcomes of LDGC CAD-CAM crowns over MC crowns. CONCLUSION: The high survival and success rates, low number of complications, and the high level of patients' acceptance of monolithic LDGC CAD-CAM crowns lend them well as predictable and viable alternatives to the "gold standard" MC crowns.
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 | 0.004 |
| 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) | 0.001 | 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".