Marginal Fit of Lithium Disilicate Crowns Fabricated Using Conventional and Digital Methodology: A Three‐Dimensional Analysis
Bibliographic record
Abstract
PURPOSE: To compare the marginal fit of lithium disilicate (LD) crowns fabricated with digital impression and manufacturing (DD), digital impression and traditional pressed manufacturing (DP), and traditional impression and manufacturing (TP). MATERIALS AND METHODS: Tooth #15 was prepared for all-ceramic crowns on an ivorine typodont. There were 45 LD crowns fabricated using three techniques: DD, DP, and TP. Microcomputed tomography (micro-CT) was used to assess the 2D and 3D marginal fit of crowns in all three groups. The 2D vertical marginal gap (MG) measurements were done at 20 systematically selected points/crown, while the 3D measurements represented the 3D volume of the gap measured circumferentially at the crown margin. Frequencies of different marginal discrepancies were also recorded, including overextension (OE), underextension (UE), and marginal chipping. Crowns with vertical MG > 120 μm at more than five points were considered unacceptable and were rejected. The results were analyzed by one-way ANOVA with Scheffe post hoc test (α = 0.05). RESULTS: ) was not significantly different from the other groups. The occurrence of underextension error was higher in DP (6.25%) and TP (5.4%) than in DD (0.33%) group, while overextension was more frequent in DD (37.67%) than in TP (28.85%) and DP (18.75%) groups. Overall, 4 out of 45 crowns fabricated were deemed unacceptable based on the vertical MG measurements (three in TP group and one in DP group; all crowns in DD group were deemed acceptable). CONCLUSION: The results suggested that digital impression and CAD/CAM technology is a suitable, better alternative to traditional impression and manufacturing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".