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Record W4408343087 · doi:10.1016/j.ejwf.2025.01.003

Overcoming three-dimensional challenges through objective decomposition and virtual-digital design: A multidisciplinary case on hypodontia treatment

2025· article· en· W4408343087 on OpenAlexfundno aff
Yubohan Zhang, Haolin Zhang, Meng Meng, Jie Gao

Bibliographic record

VenueJournal of the World Federation of Orthodontists · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicdental development and anomalies
Canadian institutionsnot available
FundersSchool of Medicine, University of New MexicoFourth Military Medical UniversityRéseau de Recherche en Santé Buccodentaire et OsseuseASCLS Education and Research Fund
KeywordsHypodontiaMultidisciplinary approachMedicineDecompositionOrthodonticsDentistry

Abstract

fetched live from OpenAlex

A 15-year-old female sought multidisciplinary treatment for hypodontia, three-dimensional problems, deep overbite, multiple occlusal interferences, and collapse of the occlusion. Through virtual-digital design with objective decomposition, this patient underwent five steps of orthodontic treatment under the guidelines of the principles: removal of occlusal restriction is a prerequisite; transverse problems are solved first; then sagittal problems; and vertical correction runs through the treatment. After orthodontic and prosthetic treatment, aesthetic appearance and functional occlusion were achieved. In complex multidisciplinary cases with three-dimensional problems, which makes it difficult to formulate the final goal, virtual-digital design under objective composition and standardized comprehensive principle are effective and necessary.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0060.003
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 head0.025
GPT teacher head0.300
Teacher spread0.275 · 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 designCase report
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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