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Record W2022513414 · doi:10.1055/s-0030-1262313

Measuring Patient-Reported Outcomes in Facial Aesthetic Patients: Development of the FACE-Q

2010· article· en· W2022513414 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

VenueFacial Plastic Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsForeheadMedicinePatient satisfactionConstruct (python library)Set (abstract data type)NosePerspective (graphical)Quality of life (healthcare)Quality (philosophy)Facial expressionFace (sociological concept)PsychologySurgeryNursingArtificial intelligenceComputer scienceCommunication

Abstract

fetched live from OpenAlex

To support the development of new techniques and technology in facial aesthetics, sophisticated ways of measuring outcomes are needed. The objective of this study was to develop the content of a set of patient-reported outcome (PRO) scales for use with facial aesthetic patients. A literature review, patient interviews, and input from experts working with facial aesthetic patients were used to develop a conceptual framework for the outcomes deemed important to facial aesthetic patients and to construct items and a set of preliminary PRO scales. The conceptual framework includes the following themes: satisfaction with facial appearance; health-related quality of life; recovery, early life impact, and adverse effects; and satisfaction with process of care. Separate scales were developed for all parts of the face (e.g., nose, ears, forehead, cheeks, etc.) rather than for particular facial procedures. This new PRO instrument, called the FACE-Q, contains multiple independently scoreable scales with preoperative and postoperative versions. Once psychometric evaluation is completed, the FACE-Q will provide researchers and physicians with the necessary tools to measure the impact and effectiveness of facial aesthetic procedures from the patients' perspective. The FACE-Q has the potential to support advocacy, quality metrics, and an evidence-based approach to facial aesthetic practice.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.236
Teacher spread0.204 · 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