Quantitative measurement of tooth and ceramic wear: in vivo study.
Bibliographic record
Abstract
PURPOSE: The aim of this study was to quantitatively measure tooth and ceramic wear over a 2-year period using a novel superimposition technique. Three ceramic systems--experimental hot-pressed ceramic (EC), Procera AllCeram (PA), and metal-ceramic--were used. MATERIALS AND METHODS: A total of 90 posterior crowns in 48 patients were randomized into 3 groups, and impressions were made at baseline and at 6-month intervals for 2 years. Clinical images were taken after using a dye to highlight surface changes. The impressions were digitized and modeled as superimposable 3-dimensional colored surface images. The depth of wear at the occlusal contact areas was quantitatively measured at 6, 12, 18, and 24 months. RESULTS: The quantitative evaluation showed more wear in Procera AllCeram at the occlusal contact areas, whereas the experimental and metal-ceramic systems showed less wear. There was a significant difference in the amount of enamel worn between all types of restorations (P < .05). There was a statistically significant difference (P < .05) in the mean depth of wear between all systems. CONCLUSIONS: The metal-ceramic and experimental systems showed less change, indicating improved wear resistance compared with Procera AllCeram. In addition, enamel opposing metal-ceramic and experimental crowns showed less wear compared to enamel opposed by Procera AIICeram crowns.
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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 | 0.000 | 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) | 0.002 | 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".