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Effect of wire size on maxillary arch force/couple systems for a simulated high canine malocclusion

2014· article· en· W2005055064 on OpenAlexaff
Paul W. Major, Roger Toogood, Hisham Badawi, Jason P. Carey, Surbhi Seru

Bibliographic record

VenueJournal of Orthodontics · 2014
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMalocclusionOrthodonticsArchMaxillary canineMedicineDentistryEngineeringStructural engineering

Abstract

fetched live from OpenAlex

AIMS: To better understand the effects of copper nickel titanium (CuNiTi) archwire size on bracket-archwire mechanics through the analysis of force/couple distributions along the maxillary arch. The hypothesis is that wire size is linearly related to the forces and moments produced along the arch. MATERIALS AND METHODS: An Orthodontic Simulator was utilized to study a simplified high canine malocclusion. Force/couple distributions produced by passive and elastic ligation using two wire sizes (Damon 0.014 and 0.018 inch) measured with a sample size of 144. RESULTS: The distribution and variation in force/couple loading around the arch is a complicated function of wire size. The use of a thicker wire increases the force/couple magnitudes regardless of ligation method. Owing to the non-linear material behaviour of CuNiTi, this increase is less than would occur based on linear theory as would apply for stainless steel wires. CONCLUSIONS: The results demonstrate that an increase in wire size does not result in a proportional increase of applied force/moment. This discrepancy is explained in terms of the non-linear properties of CuNiTi wires. This non-proportional force response in relation to increased wire size warrants careful consideration when selecting wires in a clinical setting.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0020.000

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 head0.011
GPT teacher head0.291
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations14
Published2014
Admission routes1
Has abstractyes

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