Restoration of endodontically treated teeth with major hard tissue loss – bond strength of conventionally and adhesively luted fiber‐reinforced composite posts
Bibliographic record
Abstract
AIM: To evaluate the influence of the luting system on the pull-out bond strength of conventionally and adhesively luted fiber-reinforced composite posts (FRCPs). MATERIAL AND METHODS: One hundred extracted bovine teeth (Di 3) were endodontically treated and randomly assigned to nine test groups and one control group (n = 10) according to luting system. After preparing the post cavities (8 mm), the custom-made FRCPs were inserted using conventional glass-ionomer cement (Ketac Cem), resin-reinforced glass-ionomer cement (Meron Plus and Fuji Plus), self-adhesive resin cement (RelyX Unicem and BisCem), self-conditioning adhesive and resin cement [Multilink Primer + Multilink and AdheSE + DC Activator + MultiCore Flow, or etch-and-rinse adhesive and resin cement (SealBond Ultima + CoreCem, and LuxaBond + LuxaCore Z). As a control, custom-made titanium posts were inserted with Ketac Cem. After water storage (37°C, 24 h, dark), the pull-out test was performed, followed by failure mode evaluation. The data were statistically analyzed (α = 0.05) using analysis of variance and the Dunnett T3 post hoc test. RESULTS: Luting system type and identity significantly influenced bond strength (P < 0.001); the bond strengths of all luting systems except Ketac Cem, MeronPlus, and BisCem were significantly higher than the control (4.4 ± 1.1 MPa). RelyX Unicem (12.0 ± 3.0 MPa) and LB + LCZ (14.8 ± 2.3 MPa) generated the highest bond strengths. The clinical application was demonstrated by restoring a traumatized tooth with extensive coronal destruction and thin root canal walls, using a FRCP combined with direct composite build-up. CONCLUSION: Luting system selection significantly influenced the bond strength of conventionally and adhesively luted FRCPs to bovine root canal dentin.
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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 | 0.000 |
| 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".