Fatigue resistance of ProTaper gold exposed to high-concentration sodium hypochlorite in double curvature artificial canal
Bibliographic record
Abstract
This study aimed to evaluate and compare the fatigue resistance of ProTaper Gold (PTG) and ProTaper Universal (PTU) in artificial single and double curvature canals in 5% sodium hypochlorite (NaOCl) at body temperature (37 °C). PTG and PTU files (size F1) were subjected to fatigue tests in two different artificial ceramic canals. The single curvature model had a 60° curvature angle with a 5 mm radius. The double curvature model had a 60° curvature angle with a 5 mm radius and a second 30° curvature with a 2 mm radius. A file segment was introduced into the artificial canal and immersed in water or 5% NaOCl at 37 °C. The total number of cycles to fracture (NCF) was recorded. Data were analyzed using t-test and linear regression analysis. The NCF of all files was significantly influenced by the type of NiTi metal alloy (P < .01), canal curvatures (P < .01), and the environmental conditions (P < .05). PTG had higher fatigue resistance than PTU files in both single and double curvature canals (P < .05). The NCF of PTU files in 5% NaOCl was shorter than that in water (P < .05). The mean length of broken PTG was significantly shorter than those of PTU files in both single and double curvature canals (P < .01). The fatigue performance of PTG is better than that of PTU in both single and double curvature. Environmental conditions may affect the fatigue behavior of PTU files with single curvature.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".