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A Validated Lip Fullness Grading Scale

2008· article· en· W2006063616 on OpenAlexaff
Alastair Carruthers, Jean Carruthers, Bhushan Hardas, Mandeep Kaur, ROMAN GOERTELMEYER, Derek Jones, Berthold Rzany, Joel L. Cohen, Martina Kerscher, Timothy C. Flynn, Corey S. Maas, Gerhard Sattler, Alexander Gebauer, Rainer Pooth, KATHLEEN MCCLURE, ULLI SIMONE-KORBEL, Larry Buchner

Bibliographic record

VenueDermatologic Surgery · 2008
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntraclass correlationGrading scaleIntra-rater reliabilityGrading (engineering)Inter-rater reliabilityOrthodonticsUpper lipMedicineRating scaleScale (ratio)Bivariate analysisReproducibilityStatisticsMathematicsSurgeryAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVES: To develop the Lip Fullness Grading Scale for objective quantification of lip volume for a reliable assessment and to establish the reliability of this photonumeric scale for clinical research and practice. MATERIALS AND METHODS: A 5-point photonumeric rating scale was developed to objectively quantify fullness of upper and lower lip separately. Nine experts rated photographs of 35 subjects, twice, separately for upper and lower lip. Inter- and intrarater variability was assessed by computing intraclass correlation coefficients. RESULTS: Agreement between the experts was high. Bubble plots (bivariate scatter plots) demonstrated linearity in judgment by the experts. CONCLUSION: The 5-point photonumeric scale generated spans the fullness of the upper and lower lip for which patients commonly seek correction. This scale is well stratified, with low intra- and interrater variability.

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)0.005
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.070
GPT teacher head0.277
Teacher spread0.208 · 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

Citations70
Published2008
Admission routes1
Has abstractyes

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