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Record W4312179784 · doi:10.1111/aej.12733

A novel model to evaluate the fatigue resistance of <scp>NiTi</scp> instruments: Rotational and axial movement at body temperature

2022· article· en· W4312179784 on OpenAlex
Evan O. Baird, Xiangya Huang, He Liu, Ahmed Hieawy, N. Dorin Ruse, Zhejun Wang, Markus Haapasalo, Ya Shen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian Endodontic Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational health in dentistry
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNickel titaniumMaterials scienceMovement (music)Resistance (ecology)Structural engineeringComposite materialEngineeringPhysicsShape-memory alloyAcousticsBiologyEcology

Abstract

fetched live from OpenAlex

To develop a model to test cyclic fatigue resistance of TruNatomy instruments undergoing rotational and axial movement at body temperature. A total of 288 Prime and Medium instruments were subjected to cyclic fatigue testing in simulated canals (at 37°C) using a model with either rotational movement only or rotational and axial movement simultaneously. Two different sized canals and three different types of curvatures were tested for each instrument (30/0.04 and 30/0.06 for Prime; 38/0.04 and 40/0.06 for Medium). The number of cycles to failure (fatigue resistance) was recorded. Rotational and axial movement of instruments led to greater fatigue resistance compared with rotational movement alone. Apical curvatures led to greater fatigue resistance than curvatures in the coronal and middle third. The developed dynamic model at body temperature to evaluate fatigue resistance of instrument closer simulates clinical scenarios.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.111
GPT teacher head0.421
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it