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Record W4416441193 · doi:10.1016/j.ortho.2025.101086

In vivo 3D apical displacement of mandibular incisors and canines within the symphyseal region: A CBCT-based linear measurement technique (mm) — Validation study in five patients

2025· article· en· W4416441193 on OpenAlexaff
Gaston Federico Coutsiers Morell, Kevin Chen, Carlos Flores‐Mir

Bibliographic record

VenueInternational Orthodontics · 2025
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsPetro-CanadaUniversity of Alberta
Fundersnot available
KeywordsDisplacement (psychology)Mandibular lateral incisorIncisorIn vivoAnterior teethLinear relationship

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study is to develop and validate a technique that orthodontic clinicians and researchers could use to measure lower anterior teeth apical displacement through CBCT reconstructions. MATERIAL AND METHODS: This is an in vivo study in which lower incisors, canines, and symphyses of five randomly selected patients were three-dimensional (3D) reconstructed through an automatic segmentation with a manual refinement process. All the images were generated from 0.3mm voxel size cone-beam computed tomography (CBCT) imaging. A 3D coordinate system based on CBCT segmentation and landmarks allowed a spatial localization of the apices and quantification of their displacement during orthodontic treatment. The primary outcomes were the intra- and inter-reliabilities and the respective measurement errors. RESULTS: The overall results showcased good to excellent reliability for both intra- and inter-rater reliability analyses (9.91% and 7.33% measurement errors respectively). CONCLUSION: The proposed technique could be used for 3D measurements of the apical displacement of lower anterior teeth during orthodontic treatment. The identified measurement error of less than 10% is not clinically relevant in most orthodontic situations.

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)0.001
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 score0.020
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.026
GPT teacher head0.311
Teacher spread0.285 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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