Micropush‐out dentine bond strength of a new gutta‐percha and niobium phosphate glass composite
Bibliographic record
Abstract
AIM: To characterize an experimental gutta-percha and niobium phosphate glass composite (GNB) applied with a thermoplastic technique to the root canals without sealer in a moist environment and to evaluate its micropush-out bond strength to root canal wall dentine. METHODOLOGY: The root canals of sixty human mandibular pre-molars were prepared using rotary NiTi instruments and irrigation with sodium hypochlorite and EDTA. The teeth were then randomly divided into three groups according to the root filling material used: AH plus sealer and gutta-percha (AH), EndoSequence BC gutta-percha without sealer (GBC), and GNB without sealer. The root canals were filled with a single cone using warm vertical condensation. Push-out bond strengths associated with the filling materials in slices from middle root thirds was determined 30 days after root filling. The failure mode was analyzed with SEM. Analysis using EDX and SEM-EDS was carried out to verify the composition and distribution of the particles of the tested materials. Data were statistically analyzed by one-way anova and Tukey's test (P < 0.05). RESULTS: AH and GNB groups had bond strengths of 2.83 ± 0.64 MPa and 2.68 ± 0.84 MPa, respectively, with no significant difference between them (P > 0.05). The GBC group had the lowest mean bond strength (1.34 ± 0.42 MPa), which was significantly different compared with the other groups (P < 0.05). Cohesive failures prevailed in the AH group, whereas failures were mixed in the GBC and GNB groups. The SEM-EDS analysis on the surface and in the bulk of GBC revealed only a superficial coating of bioceramic particles. Glass particles were detected both on the surface and in the bulk of GNB. CONCLUSIONS: The experimental root filling composite (GNB) had an ability to adhere to root canal wall dentine equal to the current gold standard root filling with gutta-percha and sealer (AH Plus).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".