The Effectiveness of Different Irrigation Techniques on Debris and Smear Layer Removal in Primary Mandibular Second Molars: An In Vitro Study
Bibliographic record
Abstract
Aims:The aim of the present in vitro study was to compare the effectiveness of passive ultrasonic irrigation (PUI), sonic irrigation, and mechanic dynamic activation on the removal of debris and smear layer from primary mandibular second molars during pulpectomy.Materials and methods: Mesial roots of 48 primary mandibular second molars were prepared with an R-motion 21 mm file (30/0.04)(FKG Dentaire SA, La Chaux-de-Fonds, Switzerland), irrigated with 1% sodium hypochlorite (NaOCl) and 17% ethylenediaminetetraacetic acid (EDTA), and divided into four groups (n = 24 canals) according to the final irrigation activation technique: control group without activation, PUI with Ultra-X (Eighteeth, Changzhou, China), mechanical activation with XP-endo Finisher (FKG), and sonic irrigation with EQ-S (Meta Biomed, Chungcheongbuk-do, Korea).The roots were split longitudinally and analyzed using scanning electron microscopy (SEM).The presence of debris and smear layer was assessed using a 5-grade scoring scale with 200 and 1000 magnification, respectively.The Kruskal-Wallis and Friedman tests were used for data analysis.Results: The activation of the irrigant significantly improved debris and smear layer removal (p < 0.001).There was no significant difference between Ultra-X, XP-endo Finisher, and EQ-S (p > 0.05).No activation technique was able to completely eliminate debris and smear layer from the root canals of primary mandibular second molars.Conclusions: During pediatric pulpectomy, the irrigation protocol must include activation of the irrigation solutions using either ultrasonic, sonic, or mechanical activation techniques to enhance the removal of debris and smear layer for a better prognosis.Clinical significance: During root canal treatment on primary teeth, the clinician must incorporate an activation technique in the irrigation protocol to enhance the removal of debris and smear layer and increase the success of the treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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 | 0.001 |
| 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".