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Record W4313635041 · doi:10.2319/012011-41.1

Analysis of maxillary arch force/couple systems for a simulated high canine malocclusion:

2011· article· en· W4313635041 on OpenAlexaff
Jonathan Fok, Roger Toogood, Hisham Badawi, Jason P. Carey, Paul W. Major

Bibliographic record

VenueThe Angle Orthodontist · 2011
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMalocclusionArchOrthodonticsMaxillary canineMedicineDentistryEngineeringStructural engineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To better understand the mechanics of bracket/archwire interaction through analysis of force and couple distribution along the maxillary arch using elastic ligation and to compare these results with passive ligation. MATERIALS AND METHODS: An orthodontic simulator was used to study a high canine malocclusion. Force and couple distributions produced by elastic ligation and round wire were measured. Forces and couples were referenced to the center of resistance of each tooth. Tests were repeated for 12 bracket sets with 12 wires per set. Data were compared with those derived from similar tests for passive ligation. RESULTS: Propagation of the force/couple systems around the arch using elastic ligation was extensive. Elastic ligation produced significantly more resistance to sliding, contributing to higher forces and couples at the center of resistance than were observed for passive ligation. CONCLUSIONS: The results of this study suggest some potential mechanical advantages of passive over elastic ligation. In particular, limited propagation around the arch in passive ligation reduces the occurrence of unwanted force/couple systems compared with elastic ligation. These advantages may not transfer to a clinical setting because of the conditions of the tests; additional testing would be required to determine whether these advantages can be generalized.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.050
GPT teacher head0.286
Teacher spread0.237 · 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

Citations18
Published2011
Admission routes1
Has abstractyes

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