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Record W2174112030 · doi:10.1001/jamafacial.2015.1445

Development and Psychometric Evaluation of the FACE-Q Scales for Patients Undergoing Rhinoplasty

2015· article· en· W2174112030 on OpenAlexaffabout
Anne F. Klassen, Stefan Cano, Charles East, Stephen B. Baker, Lydia Badia, Jonathan A. Schwitzer, Andrea L. Pusic

Bibliographic record

VenueJAMA Facial Plastic Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsMcMaster University
FundersNational Cancer InstitutePlastic Surgery Foundation
KeywordsRhinoplastyMedicineNosePatient-reported outcomeCronbach's alphaChecklistRasch modelPatient satisfactionOtorhinolaryngologyFace validityPhysical therapyConstruct validityPsychometricsAdverse effectQuality of life (healthcare)SurgeryClinical psychologyPsychologyNursingDevelopmental psychology

Abstract

fetched live from OpenAlex

IMPORTANCE: Rhinoplasty continues to rank among the most popular cosmetic surgical treatments. Measuring what the nose looks like has typically involved the use of observer-reported or physician-reported outcome measures (eg, photographs). While objective outcomes are important, facial appearance is subjective, and asking patients what they think about the appearance of their nose is of paramount importance. The patient perspective can be measured using patient-reported outcome instruments. OBJECTIVE: To describe the development and psychometric evaluation of the FACE-Q scales and adverse effects checklist designed to measure rhinoplasty outcomes. DESIGN, SETTING, AND PARTICIPANTS: A questionnaire was completed by patients recruited between July 13, 2010, and March 1, 2015. Psychometric methods were used to select the most clinically sensitive items for inclusion in item-reduced scales as well as to examine reliability, validity, and ability to detect clinical change. The setting was plastic surgery clinics in the United States, England, and Canada. Participants were preoperative and postoperative patients 18 years or older undergoing rhinoplasty. MAIN OUTCOMES AND MEASURES: Responses and validation measures of the FACE-Q scales and adverse effects checklist. RESULTS: In total, 158 of 169 patients invited to participate in the study were enrolled (response rate, 93.5%). The most common adverse effect was the skin of the nose looking thick or swollen. Rasch measurement theory analysis led to the refinement of a 10-item Satisfaction With Nose Scale and a 5-item Satisfaction With Nostrils Scale. The person separation index and Cronbach α were 0.91 and 0.96, respectively, for the Satisfaction With Nose Scale and 0.89 and 0.96, respectively, for the Satisfaction With Nostrils Scale. All items had ordered thresholds and good item fit. Satisfaction with the nose and nostrils was incrementally lower in participants bothered by specific adverse effects (eg, the skin of the nose looking thick or swollen). Patient satisfaction on the Satisfaction With Nose Scale and the Satisfaction With Nostrils Scale and on 3 additional FACE-Q scales (ie, Satisfaction With Facial Appearance Scale, Psychological Function Scale, and Social Function Scale) was higher after surgery than before surgery (P < .001 for all, independent samples t test). Twenty-three participants who provided preoperative and postoperative data reported improvement on all 5 scales (P ≤ .003 for all). The effect sizes ranged from 0.6 to 2.3. Significant individual-level change was reported by most participants for the Satisfaction With Nose Scale, Satisfaction With Nostrils Scale, Satisfaction With Facial Appearance Scale, and Social Function Scale. CONCLUSIONS AND RELEVANCE: A FACE-Q scales rhinoplasty module can be used in clinical practice, research, and quality improvement to incorporate the patient perspective in outcome assessments. LEVEL OF EVIDENCE: NA.

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)0.012
metaresearch head score (Gemma)0.035
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 score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.089
GPT teacher head0.297
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".

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Citations152
Published2015
Admission routes2
Has abstractyes

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