Measuring Patient-Reported Outcomes in Rhinoplasty Using the FACE-Q: A Single Site Study
Bibliographic record
Abstract
Abstract Rhinoplasty is one of the most popular surgical cosmetic treatments. Measuring the appearance of the nose has typically involved the use of observer- or surgeon-reported outcome measures (e.g., photographs). While objective outcomes are important, facial appearance is subjective, and therefore outcome assessment should incorporate the patient perspective through the use of patient-reported outcome measures. This study aims to explore relationships between FACE-Q scales scores and measuring adverse effects and change 4 months after surgery in a United Kingdom sample. A questionnaire booklet was completed by patients seeking rhinoplasty between March 2014 and March 2015. The study was conducted at a facial plastic surgery clinic office in London, United Kingdom. Pre- and postoperative rhinoplasty patients aged 19 years and older participated in the study. FACE-Q scales/checklist was utilized for the study. A total of 54 preoperative patients completed seven FACE-Q scales. Before surgery, characteristics of the nose that our sample at least satisfied (i.e., at the harder end of the clinical hierarchy) included the tip of the nose, and how the nose looked in photos and from different angles. In preoperative participants, lower scores for satisfaction with nose and/or nostrils correlated with lower satisfaction with facial appearance and appearance-related psychosocial distress. Participants (N = 13) who had surgery reported significant improvement in satisfaction with the nose, nostrils, and facial appearance overall, and improved psychological and social function. Standardized response means ranged from 0.65 (social function) to 1.55 (facial appearance). The FACE-Q rhinoplasty module can be used in clinical practice, research, and quality improvement to incorporate the patient perspective of appearance in outcome assessments. The level of evidence is defined as level III (diagnostic).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".