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Record W2316644521 · doi:10.1097/scs.0b013e3182a2e99d

Quantitative Facial Asymmetry

2014· article· en· W2316644521 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Craniofacial Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids Foundation
Fundersnot available
KeywordsFacial symmetryAsymmetryMedicineNormativePhotogrammetrySymmetry (geometry)OrthodonticsPopulationArtificial intelligenceGeometryLawPhysicsMathematicsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Although symmetry is hailed as a fundamental goal of aesthetic and reconstructive surgery, our tools for measuring this outcome have been limited and subjective. With the advent of three-dimensional photogrammetry, surface geometry can be captured, manipulated, and measured quantitatively. Until now, few normative data existed with regard to facial surface symmetry. Here, we present a method for reproducibly calculating overall facial symmetry and present normative data on 100 subjects. METHODS: We enrolled 100 volunteers who underwent three-dimensional photogrammetry of their faces in repose. We collected demographic data on age, sex, and race and subjectively scored facial symmetry. We calculated the root mean square deviation (RMSD) between the native and reflected faces, reflecting about a plane of maximum symmetry. We analyzed the interobserver reliability of the subjective assessment of facial asymmetry and the quantitative measurements and compared the subjective and objective values. We also classified areas of greatest asymmetry as localized to the upper, middle, or lower facial thirds. This cluster of normative data was compared with a group of patients with subtle but increasing amounts of facial asymmetry. RESULTS: We imaged 100 subjects by three-dimensional photogrammetry. There was a poor interobserver correlation between subjective assessments of asymmetry (r = 0.56). There was a high interobserver reliability for quantitative measurements of facial symmetry RMSD calculations (r = 0.91-0.95). The mean RMSD for this normative population was found to be 0.80 ± 0.24 mm. Areas of greatest asymmetry were distributed as follows: 10% upper facial third, 49% central facial third, and 41% lower facial third. Precise measurement permitted discrimination of subtle facial asymmetry within this normative group and distinguished norms from patients with subtle facial asymmetry, with placement of RMSDs along an asymmetry ruler. CONCLUSIONS: Facial surface symmetry, which is poorly assessed subjectively, can be easily and reproducibly measured using three-dimensional photogrammetry. The RMSD for facial asymmetry of healthy volunteers clusters at approximately 0.80 ± 0.24 mm. Patients with facial asymmetry due to a pathologic process can be differentiated from normative facial asymmetry based on their RMSDs.

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.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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