Comparative Analysis of Various Cavosurface Margins in Class <scp>II</scp> Restorations Using <scp>3D</scp> Finite Element Method
Bibliographic record
Abstract
The primary aim of these analyses was to evaluate the mechanical characteristics of the restored proximal surface of the lower first molar by comparing four different preparation designs: (a) slot preparation, (b) slot preparation with bevel, (c) slot preparation with bevel and rounded proximal box corners (RPBC), and (d) slot preparation with bevel, rounded proximal box corners, and gingival bevel (GB). The finite element method was utilized to assess various load scenarios applied to slot and bevelled restorations prepared using adhesive restorative materials. The numerical analysis revealed higher tensile stresses by up to 15 MPa when normal traction was applied at the interface between enamel and slot preparations than at the interface between enamel and bevelled preparations. However, the beveled restorations showed increased shear stresses in their thin beveled regions. The results imply a risk of separation for slot restorations. Conversely, incorporating a bevel (with or without RPBC and GB) significantly decreased normal stresses on the restoration edge and shifted it predominantly to compressive stresses. Thus, bevelled restorations may be less prone to debonding at their edges under occlusal loads. However, they may still be susceptible to shear debonding when locally loaded on their thin-beveled regions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".