Evaluation of the Impact of Different Instrumentation Techniques on the Incidence of Postoperative Pain in Patients Undergoing Root Canal Treatment
Bibliographic record
Abstract
BACKGROUND: Postoperative pain is a common concern in root canal treatment, and the choice of instrumentation technique can significantly impact patient comfort. This study aimed to evaluate the impact of different instrumentation techniques on the incidence of postoperative pain in patients undergoing root canal treatment. METHODS: A randomized controlled trial was conducted on 208 patients randomly assigned to four groups: step-back preparation, crown-down preparation, hybrid technique, and conventional instrumentation. Pain intensity was assessed using a verbal rating scale (VRS) at six, 12, 24, 48, and 72 hours postoperatively. Data were analyzed using appropriate statistical methods. RESULTS: The mean pain scores and standard deviations (SDs) were calculated for each instrumentation technique at different time intervals. At six hours, the step-back preparation group reported a mean pain score of 2.3 (SD = 0.8), the crown-down preparation group had a score of 2.8 (SD = 0.9), the hybrid technique group had a score of 2.5 (SD = 0.7), and the conventional instrumentation group had a score of 3.1 (SD = 0.1). The differences in pain scores between the groups were statistically significant at all time intervals (p < 0.05). CONCLUSION: The choice of instrumentation technique significantly influenced the incidence of postoperative pain in root canal treatment. The step-back preparation technique was associated with lower pain intensity than the crown-down preparation, hybrid technique, and conventional instrumentation. These findings highlight the importance of considering the instrumentation technique to optimize patient comfort during and after root canal treatment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".