Torque Generation of the Endodontic Instruments: A Narrative Review
Bibliographic record
Abstract
As the use of nickel-titanium (NiTi) file systems for root canal therapy has become popular; hence, knowledge and understanding of the characteristics of NiTi files is essential for dentists. Unintended sudden fracture can occur during root canal shaping, and it is important to understand the conditions that may cause instrument fractures. Torque is defined as the force required to rotate the NiTi file and can be considered of as a parameter for the stress generated. The endodontic engine maintains a constant rotational speed by adjusting torque regardless of the root canal conditions. The process of root canal shaping by rotary instruments is a series of actions that requires torque and generates stress to both the teeth and the NiTi instruments. The generated stress may induce the strain accumulation on NiTi instrument and the canal wall and lead to the development of microcrack in the instrument and dentinal wall. Therefore, understanding of torque and stress generated is important to prevent the fractures to the instrument and the teeth. This stress has been measured using various experimental approaches, including microcrack observation by using a microscope or computed tomography, attaching strain gauges to the teeth, and finite element analysis. This review focuses on the stress generated to the teeth and the instrument during instrumentation under various experimental conditions. The factors related to torque generation are also discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".