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Record W2944733890 · doi:10.1111/ocr.12304

Analysis of facial skeletal asymmetry during foetal development using μCT imaging

2019· article· en· W2944733890 on OpenAlexaff
Motoki Katsube, Sara Rolfe, Stephanie R. Bortolussi, Yutaka Yamaguchi, Joy M. Richman, Shigehito Yamada, Siddharth R. Vora

Bibliographic record

VenueOrthodontics and Craniofacial Research · 2019
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Dental and Craniofacial ResearchJapan Society for the Promotion of Science
KeywordsAsymmetryFluctuating asymmetryFacial symmetryCraniofacialAnatomyBiologyMedicineEvolutionary biologyOrthodonticsPhysics

Abstract

fetched live from OpenAlex

OBJECTIVES: Asymmetry has been noted in the human craniofacial region in several pathological conditional and growth abnormalities, often with a directional predilection. Physiological asymmetry has also been reported in normal adults and adolescents, with certain regions of the cranioskeleton, such as the mandible, displaying prevalent asymmetry. However, the timing at which such asymmetries arise has not been evaluated. The objectives of this study were to assess the degree of asymmetry in facial bones during the foetal stages of human development. MATERIAL AND METHODS: Twenty-one preserved conceptuses from the Congenital Anomaly Research Center at Kyoto University, between ages 15 and 20 weeks of gestation, were studied using high-resolution μCT imaging. Asymmetry analysis was performed on digitally segmented facial bone pairs, using geometric morphometric (GM) approaches as well as adapted deformation-based asymmetry (DBA) methods. RESULTS: GM analysis revealed that the developing facial bones display statistically significant fluctuating and directional asymmetry. DBA methods suggest that the magnitude of asymmetry in facial bones is low and does not appear to be correlated to the estimate of overall size of conceptus. Additionally, the patterns of asymmetry are highly variable between individual specimens. CONCLUSIONS: The developing foetal facial skeleton displays variable patterns of low magnitude asymmetry. GM and DBA methods offer unique advantages to assess facial asymmetry quantitatively and qualitatively.

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)0.002
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 score0.025
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.110
GPT teacher head0.400
Teacher spread0.290 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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