Cleaning and Disinfecting Oval-Shaped Root Canals: Ex Vivo Evaluation of Three Rotary Instrumentation Systems with Passive Ultrasonic Irrigation
Bibliographic record
Abstract
Background and Objectives: Successful root canal treatment depends on the thorough removal of biofilms through chemomechanical preparation. This study aimed to investigate and compare the cleaning and disinfecting efficiency of oval-shaped root canals using XP-endo Shaper (XPS), ProTaper Next (PTN), and HyFlex CM (HCM) in combination with passive ultrasonic irrigation (PUI). Materials and Methods: Ninety extracted teeth were contaminated and randomly divided into three groups: XPS, PTN, and HCM. Each group was assigned to three subgroups: subgroup A (sterile saline), subgroup B (3% sodium hypochlorite and 17% ethylenediaminetetraacetic acid), and subgroup C (3% sodium hypochlorite, 17% ethylenediaminetetraacetic acid, and PUI). Bacterial sampling was conducted both from baseline samples and samples after chemomechanical preparation. Scanning electron microscopy (SEM) was used to evaluate the residue bacterial biofilms, hard tissue debris, and smear layers on the buccolingual walls of oval-shaped root canals. Results: When combined with sterile saline, XPS demonstrated a higher reduction of bacterial counts and was more effective in eradicating Enterococcus faecalis in the middle third of the canals compared to the other instruments (p < 0.05). Additionally, when used with antimicrobial irrigants, XPS was more effective in disinfecting the coronal third of the canals than the other instruments (p < 0.05). Furthermore, XPS reduced hard tissue debris more effectively in the middle third of canals than in the apical third (p < 0.05). Conclusions: XPS outperforms PTN and HCM in disinfecting oval-shaped root canals. Despite the fact that combining XPS and PUI improves cleaning and disinfecting, removing hard tissue debris from the critical apical area remains challenging.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".