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Record W2165613091 · doi:10.4103/2278-0203.143237

Mechanical properties of cobalt-chromium wires compared to stainless steel and β-titanium wires

2014· article· en· W2165613091 on OpenAlexaff
Tarek El‐Bialy, Ahmad Alobeid, Malak Hasan, Mahmoud Al-Suleiman

Bibliographic record

VenueJournal of Orthodontic Science · 2014
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceTitaniumDeformation (meteorology)MolybdenumChromiumComposite materialStatistical analysisTitanium alloyMetallurgyAlloyMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Previous studies have reported on mechanical properties of different orthodontic wires. However, there is a paucity of information that comparing the mechanical properties of Blue Elgiloy (BE) when compared to stainless steel and TMA, as finishing wires as received by different companies. AIMS: The aim of this study was to evaluate the mechanical properties of BE wires compared to stainless steel (SS) and titanium Molybdenum alloy (TMA) also known as β titanium as provided by two companies. MATERIALS AND METHODS: Six 0.016 x 0.022-14mm-samples of each wire were fixed individually to Instron machine and were tested in loading and unloading for three times. The initial load was set for 500 Kg at a speed of 1mm/min and displacement was adjusted for (0.5, 1mm in loading and 0.5 mm unloading at 25°C). STATISTICS ANALYSIS: Variables were compared between groups by ANOVA test using SPSS statistical software. RESULTS: BE shows comparable forces to SS when loaded 0.5 and showed decreased forces in 1mm loading compared to SS, and higher than TMA. BE also showed no forces at unloading and high deformation. CONCLUSION: BE from the two companies showed comparable mechanical properties while SS and TMA were different. The deformation of BE and its decreased forces in unloading may limit its clinical use.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.294
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes1
Has abstractyes

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