Reliability of Reduced-thickness and Thinly Veneered Lithium Disilicate Crowns
Bibliographic record
Abstract
The present investigation hypothesized that the reliability of reduced-thickness monolithic lithium disilicate crowns is high relative to that of veneered zirconia (Y-TZP) and comparable with that of metal ceramic (MCR) systems. CAD/CAM first mandibular molar full-crown preparations were produced with uniform thicknesses of either 1.0-mm or 2.0-mm occlusal and axial reduction, then replicated in composite for standard crown dies. Monolithic 1.0-mm (MON) and 2.0-mm CAD/CAM lithium disilicate crowns, the latter with a buccal thin veneer (BTV) of 0.5 mm, were fabricated and then sliding-contact-fatigued (step-stress method) until failure or suspension (n = 18/group). Crack evolution was followed, and fractography of post mortem specimens was performed and compared with that of clinical specimens. Use level probability Weibull calculation (use load = 1,200 N) showed interval overlaps between MON and BTV. There was no significant difference between the Weibull characteristic failure loads of MON and BTV (1,535 N [90% CI 1,354-1,740] and 1,609 N [90% CI 1,512-1,712], respectively), which were significantly higher than that of Y-TZP (370 N [90% CI 322-427]) and comparable with that of MCR (1,304 N [90% CI 1,203-1,414]), validating the study hypothesis.
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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 | 0.002 | 0.003 |
| 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".