Influence of Autoclave Sterilization on the Resistance to Cyclic Fatigue of AF- F-ONE , ProTaper Ultimate, and ProTaper Gold NiTi Instruments (A Comparative Study)
Bibliographic record
Abstract
Aims: This investigation aimed to examine the impact of autoclave sterilization on the cyclic fatigue of AF-F ONE, ProTaper Ultimate, and ProTaper Gold. Materials and Methods: Sixty #25 rotary files were categorized into groups as follows: AF-F ONE (Group A), ProTaper Ultimate (Group B), and ProTaper Gold (Group C). Additionally, within each group, there were subdivisions into sterilized and non-sterilized subgroups. Sterilized instruments underwent five cycles of sterilization by autoclave. Cyclic fatigue resistance was evaluated using a simulated canal featuring a single curvature (60° angle, 5-mm radius). Files were operated until fracture, and time and number of cycles to fracture (NCF) were recorded. Statistical analysis: One-way ANOVA, Post hoc Tukey test, and independent t-test. Results: ANOVA indicated a significant difference among non-sterilized groups (p < 0.05). Tukey's test showing a notable difference between AF-F ONE and ProTaper Ultimate/ProTaper Gold (p < 0.05), but not between the latter two (p>0.05).AF-F ONE significantly decreased after autoclave sterilization (p < 0.05), while ProTaper Ultimate and ProTaper Gold remained unaffected (p>0.05). Conclusion: Autoclave sterilization significantly affected the cyclic fatigue of AF-F ONE, whereas ProTaper Ultimate and ProTaper Gold demonstrated resilience. Keywords: Cyclic fatigue, ProTaper Ultimate, AF-F ONE, autoclave sterilization.
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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".