Filling Material Bond Strength to Dentin Is Positively Influenced by the Agitation of Endodontic Final Irrigating Solutions.
Bibliographic record
Abstract
Introduction: The final step of irrigation has been considered to of increase the bonding strength of filling material to dentin. This study investigated the impact of three final-step irrigation methods on the endodontic sealer bond strength to dentin by using a micro push-out test. Materials and Methods: Palatal roots of human maxillary molars were cleaned and shaped and randomly divided in six groups (n=15) according to the final-step irrigation method and the type of root canal sealer used. The solutions used for the final-step irrigation were 17% ethylenediaminetetraacetic acid and 2.5% sodium hypochlorite, which underwent three methods: 1) syringe-needle irrigation/conventional, 2) passive ultrasonic irrigation, and 3) XP-endo Finisher agitation. The root canal sealers used were: EndoSequence BC Sealer, and AH-Plus sealer. Roots were obturated with the single cone technique and then, cross-sectioned in 2-mm-thick slices (3 slices from each root). Push-out test was performed on the sliced specimens (cervical, middle, and apical thirds) with a universal testing machine. Bond strength values were recorded in megapascal (MPa). Subsequently, each specimen was longitudinally split to verify the type of failure. Data analysis was performed using Johnson transformation, three-way analysis of variance, Tukey's post-hoc tests, and the partial Eta squared test. Results: >0.05). Conclusion: Agitation of the final irrigating solution may improve the bonding of the sealer to canal walls. AH-Plus sealer had the highest bond strength. The bond strength reduced significantly towards the apical third.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".