Torsional Fracture Resistance of Niti Rotary Glide Path Files under Flexural Stress
Bibliographic record
Abstract
This study aimed to evaluate the effects of flexural stress on the torsional fracture resistance of NiTi glide path files. PathFile #16/02, RaCe #15/04, RaCe Evo #15/04, HyFlex EDM #15/03, TruNatomy Glider #17/02, and V Taper 2H #17/04 were examined by scanning electron microscopy (SEM) (n = 3/brand) and subjected to differential scanning calorimetry (n = 2/brand). Torsional fracture resistance testing was performed in straight (ISO 3630-1) and flexural modes (n = 15/brand/mode). Flexural mode testing involved instruments rotating within a stainless-steel artificial double-curved canal. Ultimate strength and distortion angle until failure were recorded, and fractured instruments were examined by SEM. Statistical analyses involved independent sample t-test and one-way analysis of variance with Games–Howell pots hoc test. Austenitic transformation- finishing temperatures of heat-treated files were above body temperature. For RaCe Evo, HyFlex EDM, TruNatomy Glider, and V Taper 2H, the flexural mode resulted in a significantly higher distortion angle compared to the straight mode (p < 0.05). The maximum torque of RaCe Evo, HyFlex EDM increased with the flexural stress (p < 0.05). V taper 2H showed the highest distortion angle and ultimate strength. SEM showed typical patterns of torsional fracture for all tested files. The flexural stress positively affected distortion angle of heat-treated NiTi glide path files.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".