Evaluation of varied repair protocols applied to aged composite resin.
Bibliographic record
Abstract
PURPOSE: This paper aims to examine varied surface treatments combined with adhesive bonding to ascertain the highest bond strength of the composite resin repair. MATERIALS AND METHODS: A total of 100 specimens was made using an increment technique. The prepared specimens were aged for 20 days in 37 degrees +/- 20 degrees C water and then kept for 24 h at room temperature. The samples were separated into 5 groups of 20, and each group then received a different surface treatment (control group, phosphoric acid, diamond bur, air abrasion, silane primer combined with a diamond bur treatment). The effect of each surface treatment on the bond strength was determined by a shear bond test. RESULTS: Specimens that received surface treatment with a diamond bur exhibited higher shear bond strengths than any of the other repaired groups (p < 0.05). Those treated with air abrasion had the second highest shear bond strengths, but without a statistically significant difference when compared to the control group (p > 0.05). Specimens treated with phosphoric acid showed virtually no difference compared to the specimens of the control group (p > 0.05). CONCLUSION: Statistical analysis revealed that surface treatment with a diamond bur results in higher shear bond strength than other tested methods.
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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) | 0.002 | 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".