Effect of autoclave sterilization on the cyclic fatigue resistance of thermally treated Nickel–Titanium instruments
Bibliographic record
Abstract
AIM: To compare the cyclic fatigue resistance of HyFlex CM, Twisted Files (TF), K3XF, Race, and K3, and evaluate the effect of autoclave sterilization on the cyclic fatigue resistance of these instruments both before and after the files were cycled. METHODOLOGY: Five types of NiTi instruments with similar size 30, .06 taper were selected: HyFlex CM, TF, K3XF, Race and K3. Files were tested in a simulated canal with a curvature of 60° and a radius of 3 mm. The number of cycles to failure of each instrument was determined to evaluate cyclic fatigue resistance. Each type of instruments was randomly divided into four experimental groups: group 1 (n = 20), unsterilized instruments; group 2 (n = 20), pre-sterilized instruments subjected to 10 cycles of autoclave sterilization; group 3 (n = 20), instruments tested were sterilized at 25%, 50% and 75% of the mean cycles to failure as determined in group 1, and then cycled to failure; group 4 (n = 20), instruments cycled in the same manner as group 3 but without sterilization. The fracture surfaces of instruments were examined by scanning electron microscopy (SEM). RESULTS: HyFlex CM, TF and K3XF had significantly higher cyclic fatigue resistance than Race and K3 in the unsterilized group 1 (P < 0.05). Autoclave sterilization significantly increased the MCF of HyFlex CM and K3XF (P < 0.05) both before and after the files were cycled. SEM examination revealed a typical pattern of cyclic fatigue fracture in all instruments. CONCLUSIONS: HyFlex CM, TF and K3XF instruments composed of new thermal-treated alloy were more resistant to fatigue failure than Race and K3. Autoclaving extended the cyclic fatigue life of HyFlex CM and K3XF.
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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".