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Record W2052790810 · doi:10.1111/ocr.12024

The validity, reliability, and time requirement of study model analysis using cone‐beam computed tomography–generated virtual study models

2013· article· en· W2052790810 on OpenAlexaff
Nghe S. Luu, M. Mandich, Carlos Flores‐Mir, Tarek El‐Bialy, Giseon Heo, Jason P. Carey, Paul W. Major

Bibliographic record

VenueOrthodontics and Craniofacial Research · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCone beam computed tomographyReliability (semiconductor)Intra-rater reliabilityOrthodonticsDentistryRepeated measures designComputed tomographyMedicineMathematicsStatisticsConfidence intervalSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the validity, reliability, and time spent to perform a full orthodontic study model analysis (SMA) on cone-beam computed tomography (CBCT)-generated dental models (Anatomodels) compared with conventional plaster models and a subset of extracted premolars. SETTING AND SAMPLE POPULATION: A retrospective sample of 30 consecutive patient records with fully erupted permanent dentition, good-quality plaster study models, and CBCT scans. Twenty-two extracted premolars were available from eleven of these patients. MATERIALS AND METHODS: Five evaluators participated in the inter-rater reliability study and one evaluator for the intrarater reliability and validity studies. Agreement was assessed by ICC and cross-tabulations, while mean differences were investigated using paired-sample t-tests and repeated-measures anova. RESULTS: For all three modalities studied, intrarater reliability was excellent, inter-rater reliability was moderate to excellent, validity was poor to moderate, and performing SMA on Anatomodels took twice as long as on plaster. CONCLUSIONS: Study model analysis using CBCT-generated study models was reliable but not always valid and required more time to perform when compared with plaster models.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.124
GPT teacher head0.377
Teacher spread0.253 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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