The Influence of Nickel-Titanium (Ni-Ti) Rotary Instrument Systems on Debris and Smear Layer Formation in Endodontic Procedures: An In Vitro Scanning Electron Microscopy Study
Bibliographic record
Abstract
Background Successful endodontic treatment relies on the effective removal of debris and the prevention of smear layer formation within the root canals. The choice of nickel-titanium (Ni-Ti) rotary instrument systems can significantly impact these outcomes. Aim This study aims to evaluate and compare the debris and smear layer formation in root canals of extracted mandibular second premolar teeth following instrumentation with the ProTaper Universal (Dentsply Sirona, Charlotte, NC) (Group II), Twisted File (Kerr Endodontics, Gilbert, AZ) (Group III), and XP Endo (FKG Dentaire, La Chaux-de-Fonds, Switzerland) (Group IV) Ni-Ti rotary instrument systems. Methods In this in vitro study, 60 extracted mandibular second premolar teeth were randomly divided into four groups, each containing 15 teeth. Group I served as the control with no instrumentation. Groups II, III, and IV were instrumented with the ProTaper Universal rotary file, the Twisted File, and the XP Endo file systems, respectively. Debris and smear layer formation were evaluated through scanning electron microscopy (SEM), and photomicrographs were scored using a standardized index. Results Group II (ProTaper) exhibited the highest mean debris and smear layer scores, with values of 3.50 and 2.70, respectively. Group IV (XP Endo) demonstrated the least debris and smear layer formation, with mean scores of 2.65 and 2.08, respectively. Statistical analysis confirmed significant differences among the groups for both debris and smear layer formation. Conclusion The results highlight the practical importance of selecting appropriate Ni-Ti rotary instrument systems to minimize debris and smear layer formation during endodontic procedures. The XP Endo file system showed promise as a favorable choice in this regard, but further clinical research is needed to validate these findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".