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Record W2100263606 · doi:10.4103/2278-0203.143230

Comparison of two three-dimensional cephalometric analysis computer software

2014· article· en· W2100263606 on OpenAlexaff
Tarek El‐Bialy, Dena Sawchuk, Adel Alhadlaq, Thamer Alkhadra, Terry Carlyle, Budi Kusnoto

Bibliographic record

VenueJournal of Orthodontic Science · 2014
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOrthodonticsIntraclass correlationSoftwareStatistical analysisCephalometric analysisStatistical softwareReliability (semiconductor)Cone beam computed tomographySignificant differenceMathematicsComputer scienceComputed tomographyMedicineReproducibilityStatisticsPhysicsRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Three-dimensional cephalometric analyses are getting more attraction in orthodontics. The aim of this study was to compare two softwares to evaluate three-dimensional cephalometric analyses of orthodontic treatment outcomes. MATERIALS AND METHODS: Twenty cone beam computed tomography images were obtained using i-CAT(®) imaging system from patient's records as part of their regular orthodontic records. The images were analyzed using InVivoDental5.0 (Anatomage Inc.) and 3DCeph™ (University of Illinois at Chicago, Chicago, IL, USA) software. Before and after orthodontic treatments data were analyzed using t-test. RESULTS: Reliability test using interclass correlation coefficient was stronger for InVivoDental5.0 (0.83-0.98) compared with 3DCeph™ (0.51-0.90). Paired t-test comparison of the two softwares shows no statistical significant difference in the measurements made in the two softwares. CONCLUSIONS: InVivoDental5.0 measurements are more reproducible and user friendly when compared to 3DCeph™. No statistical difference between the two softwares in linear or angular measurements. 3DCeph™ is more time-consuming in performing three-dimensional analysis compared with InVivoDental5.0.

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)0.003
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 score0.219
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.349
Teacher spread0.316 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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