Irrigation Solution and Activation System Usage Habits of Endodontists and Endodontic Assistants in Türkiye: A Survey Study
Bibliographic record
Abstract
Objectives The aim of this study is to evaluate the irrigation application habits and approaches to irrigation activation systems used during root canal treatment by endodontists and endodontic assistants in Türkiye. Material and Methods A three-part online questionnaire was distributed to endodontic specialists and endodontic assistants in Türkiye. The survey collected demographic data, information on irrigation solution and activation system usage, and attitudes via a 28-item Likert scale. Data from 156 participants were analyzed using descriptive statistics, t-tests, and ANOVA to identify significant differences based on gender, age, and professional status. Results The most commonly used irrigant was NaOCl; 50% of participants used 2.5% concentration NaOCl, while 44% used 5.25% concentration NaOCl. EDTA (17%) and CHX (2%) were also widely used. Pulp vitality was found to influence the irrigation protocol for 66% of participants. Traditional syringe irrigation was the most preferred activation method. Significant differences in practices and attitudes were observed, particularly regarding the adoption of new activation technologies and cost considerations, depending on age and professional experience. Conclusion Endodontists in Türkiye generally follow current international standards regarding irrigation solutions. However, more experience and the adoption of modern irrigation activation systems are needed. More education and training in this area could improve clinical practices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".