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Record W3124374639 · doi:10.1080/10255842.2020.1870965

Evaluation of facial symmetry after jaw reconstruction surgery

2021· article· en· W3124374639 on OpenAlexaff
Jade Duchscherer, Daniel Aalto, Lindsey Westover

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2021
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsMisericordia Community HospitalUniversity of Alberta
Fundersnot available
KeywordsFacial symmetryAsymmetryCraniofacialSagittal planeSymmetry (geometry)Facial reconstructionSoft tissueOrthodonticsMedicineCraniofacial abnormalityAnatomySurgeryMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

The current study proposes a 3D objective method of evaluating facial symmetry after reconstructive surgery of orofacial structures. 3D models of the craniofacial and soft tissue surfaces were reflected about the mid-sagittal plane. The original model was aligned with the reflection and the best plane of symmetry was found. A deviation contour map quantified the areas of asymmetry and gave a global score of the asymmetry. The asymmetry scores were successfully obtained for 18 patients who had underwent reconstruction of lower face. The asymmetry values at craniofacial and soft tissue levels were moderately correlated (R2=0.39). Overall, the developed method effectively highlights areas of asymmetry and can help evaluate aesthetic outcomes of facial reconstruction surgery.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.002

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 head0.044
GPT teacher head0.348
Teacher spread0.304 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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