Fracture strenght and ribbond fibers: In vitro analysis of mod restorations
Bibliographic record
Abstract
Background: Ribbond fibers are supposed to be a reinforcing material in restoration of compromised teeth.This study aims to compare MOD restorations with and without Ribbond Fiber in terms of fracture strength under axial loading; to identify the minimum depth of MOD cavities to use Ribbond Fiber (to improve the fracture strength under axial load.Material and Methods: 20 upper and lower molars extracted intact were used for the experiment.The teeth were prepared with 2 types of cavities and then divided into 4 groups: 1) 5 mm deep MOD cavities with residual interaxial dentin restored without Ribbond; 2) 5 mm deep MOD cavities with residual interaxial dentin restored with Ribbond; 3) 5 mm deep MOD cavities without residual interaxial dentin, restored without Ribbond; 4) 5 mm deep MOD cavities without residual interaxial dentin restored with Ribbond.The restored teeth were then subjected to thermal cycling and their fracture strength was evaluated using an Instron device.The Mann-Whitney statistical test was used to compare fracture strength among groups.Finally, a descriptive analysis of the verified fractures was performed.Results: There was a statistically significant difference between groups 1 and 2 (P = 0.0090) in the loading force required for a fracture.In contrast, there was no statistically significant difference between groups 3 and 4 (P = 0.7540).Groups 1 and 2 had the fewest non-restorable fractures, in contrast to groups 3 and 4. Conclusions: Ribbond fiber application in MOD cavities seems to be more effective in terms of strengthening where cavities have interaxial dentinal tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".