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Torsional Properties Of New And Used Rotary K3 NiTi Files

2003· article· en· W2082741094 on OpenAlexaff
Ghassan Yared, G. K. Kulkarni, Fadia Ghossayn

Bibliographic record

VenueAustralian Endodontic Journal · 2003
Typearticle
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTorqueFracture (geology)Rotation (mathematics)Nickel titaniumMaterials scienceOrthodonticsMathematicsComposite materialPhysicsGeometryMedicineThermodynamics

Abstract

fetched live from OpenAlex

The purpose of this study was to compare torque (gcm) and angle of rotation (degrees) at fracture of new and used NiTi K3 .04 rotary instruments. The relation between size of instrument and torque at fracture was also investigated. The torque and angle of rotation at fracture of new and used NiTi K3 .04 rotary instruments sizes 15 to 40 were determined according to ANSI/ADA Specification No. 28. Analysis of variance was used to compare the torque and angle of rotation at fracture among the different sizes of the new instruments and between new and used instruments of the same size (alpha = 0.05). The relationship between torque at fracture and size of instrument was determined with a regression analysis. Torque at fracture of the new instruments increased significantly with the diameter (p < 0.05). The used instruments, sizes 25 to 40, had significantly lower torque at fracture values compared to the new ones (p < 0.05). The used instruments, sizes 20 and 35 had significantly lower angle of rotation at fracture compared to the new ones (p < 0.05). A stronger relationship was found between the size of the file and the torque at fracture for the new instruments (p < 0.0001) compared to the used ones (p < 0.0001). The results of the present study suggested that the torque at fracture values of new instruments increased significantly with the diameter. The results also suggested that repeated use of .04 K3 instruments affected mainly the torque at fracture.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.278
Teacher spread0.213 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2003
Admission routes1
Has abstractyes

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