Sealing ability of mineral trioxide aggregate (MTA) combined with distilled water, chlorhexidine, and doxycycline
Bibliographic record
Abstract
The aim of this study was to evaluate the sealing ability of mineral trioxide aggregate (MTA Bio) combined with different mixing agents (distilled water, chlorhexidine, doxycycline), used as an apical root-end filling material. Forty-two extracted human teeth were divided into three groups (n = 12); six teeth were used as controls. Root-ends were resected at 90 degrees, 3 mm from the apex. Root-end cavities were prepared using ultrasonic tips and filled with MTA Bio plus distilled water, 2% chlorhexidine solution, or 10% doxycycline solution. Apical sealing was assessed by microleakage of 50% silver nitrate solution. Roots were longitudinally sectioned in a buccolingual plane and analyzed using an operating microscope (20× magnification). Depth of dye leakage into the dentinal walls was measured in millimeters. Results were analyzed using ANOVA and Tukey's test (P = 0.05). MTA Bio plus distilled water showed significantly higher mean leakage results (1.06 mm) when compared with MTA Bio plus doxycycline (0.61 mm), and higher, although not significant, results when compared with MTA Bio plus chlorhexidine (0.79 mm). In conclusion, replacing distilled water with two biologically active mixing agents (doxycycline and chlorhexidine) did not alter the sealing properties of MTABio. The antimicrobial properties of these combinations should be further investigated.
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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) | 0.001 | 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".