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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.000 | 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 teacher head, 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".