Comparison of Success Rate by Evaluating Postoperative Pain Intensity after Root Canal Instrumentation With 'K' Hand Files and Rotary One Shape File
Bibliographic record
Abstract
Background: Root canal treatment is the procedure in which infected pulp is removed to eliminate microbial invasion and to maintain tooth form and function. It includes access cavity preparation, working length determination, adequate cleaning and shaping and obturation of root canals. Objective: To compare the success rate by assessing the level of postoperative pain following root canal instrumentation using a manual K-file against a Rotary One shape file. Material & Methods: Using a random selection process, 112 patients were split into two groups. In group A patient, root canals will be prepared by K hand files and in group B patients, root canals will be prepared by rotary one shape file. Patients were recalled after 72 hours to evaluate postoperative pain and score was recorded according to verbal rating scale (VRS). Results: Significant association was found regarding Success in both groups ((Group A (K hand files), Group B (Rotary One shape file)) with p-value = 0.036. Conclusion: Within the confines of this study, it is possible to draw the conclusion that the hand K-file was found to have a significantly higher success rate for postoperative pain intensity after root canal instrumentation when compared to the Rotary One shape file and significant association was found between hand K- file and Rotary One shape file in terms of pain after endodontic 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 | 0.002 | 0.007 |
| 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) | 0.002 | 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".