Effect of Different Agitation Techniques on the Penetration of Irrigant and Sealer into Dentinal Tubules
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare the effectiveness of five intracanal agitation techniques on the penetration of irrigant and sealer into dentinal tubules. BACKGROUND DATA: Intracanal agitation techniques could promote chemomechanical debridement and the sealing of root canals during endodontic treatment. However, there is limited evidence for the agitation effect of Nd:YAP laser. MATERIALS AND METHODS: NiTi rotary files (Dentsply Maillefer) up to X4 (ISO 40 size). Rhodamine B-labeled sodium hypochlorite was used for final irrigation together with the conventional syringe (control), sonic, ultrasonic, Nd:YAP laser, or V-Clean™ endodontic agitation system. All canals were obturated with gutta-percha and fluorescein isothiocyanate-labeled AH Plus sealer. Transverse sections were obtained at 2, 5, and 8 mm from the apex and observed under confocal laser scanning microscopy. Maximum penetration depth and penetration percentage of both irrigant and sealer were recorded. Kruskal-Wallis and Mann-Whitney tests were performed for multiple comparisons. The Spearman coefficient was calculated to confirm correlations between irrigant and sealer penetration. RESULTS: Laser agitation attained the most irrigant and sealer penetration depth and penetration percentage (p < 0.05). Sonic and ultrasonic agitation attained significantly more penetration percentage than the control group at 2 mm from the apex (p < 0.05). Patterns of irrigant and sealer penetration correlated significantly for all agitation techniques (p < 0.001). CONCLUSIONS: Nd:YAP laser was superior to other agitation techniques in dentinal tubule penetration of irrigant and sealer at one or more sectioned levels from the apex.
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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) | 0.001 | 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) | 0.001 | 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".