Measuring Patient-Reported Outcomes in Facial Aesthetic Patients: Development of the FACE-Q
Bibliographic record
Abstract
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.
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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".