Torsional fatigue and endurance limit of a size 30 .06 ProFile rotary instrument
Bibliographic record
Abstract
AIM: To evaluate the torsional cyclic fatigue characteristics and specifically the endurance limit (EL) of a nickel-titanium rotary instrument. METHODOLOGY: Size 30.06 taper ProFile instruments were evaluated. The equipment was assembled according to the ANSI/ADA Specification No. 28. The motor was programmed to repeatedly rotate to a selected deflection angle (DA) and then return to zero (cycle). Testing started at 200 degrees and was continued at decreasing angles until 10(6) cycles were reached without instrument fracture. Ten instruments were tested at each DA. The mean log number of cycles to fracture and standard deviation were determined for each DA at which fracture occurred. The DA at which 10(6) cycles were reached without instrument fracture corresponded by definition to the EL. Analysis of variance and pairwise comparisons using Duncan's multiple range test were performed to detect significant differences among the mean log number of cycles of the different DA. Significance was determined at the 95% confidence level. RESULTS: Instruments cycled at larger DA consistently demonstrated fewer cycles to fracture than those cycled at smaller DA. The differences among the mean log number of cycles of the different DA were statistically significant (P < 0.001). Cycles of 10(6) were completed without instrument fracture at 2.5 degrees. CONCLUSIONS: A torsional fatigue profile was generated for a specific NiTi rotary instrument. The EL was 2.5 degrees.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".