Coronal micro leakage of lingual access restoration in anterior teeth - an in-vitro study
Bibliographic record
Abstract
Aims: To evaluate in vitro the extent of coronal microleakage in endodontically treated anterior teeth using combination i.e., glass ionomer and composite resin materials with zinc oxide eugenol, zinc phosphate and zinc polycarboxylate reinforced with tannic acid and zinc fluoride. Materials and Method: An in vitro study was conducted comparing two different permanent restorative materials and three different base materials to restore lingual access cavity of 92 endodontically treated permanent maxillary anterior teeth. Linear dye penetration was measured along the tooth restoration interface in 92 sectioned teeth. The positive control tooth exhibited leakage involving the total length of gutta percha. The negative control tooth did not demonstrate any leakage. Mean length of restorative materials, base materials, mean leakage of restorative materials and base materials as well as mean length of gutta percha were evaluated by analysis of variance. Results: The positive control tooth exhibited leakage involving the total length of gutta percha. The negative control tooth did not demonstrate any leakage. The results showed least penetration of silver nitrate dye in teeth restored with composite resin over glass ionomer base. Conclusion: In conclusion maximum linear dye penetration was measured in teeth restored with glass ionomer restoration over zinc phosphate cement base.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".