Measuring Outcomes in Aesthetic Surgery: A Comprehensive Review of the Literature
Bibliographic record
Abstract
Outcomes research examines the end results of medical interventions, taking into account patients' experiences, preferences, and values. The purpose of assessing outcomes is to provide evidence on which to base clinical decisions. The assessment of outcomes in aesthetic surgery is especially pertinent because patient satisfaction is the predominant factor in determining success. In cosmetic surgery, various scales have been used to assess outcomes. Unfortunately, none of these methods has achieved widespread use. The adoption of broadly accepted, relevant scales to measure outcomes would be advantageous, because this would allow the comparison of techniques, quantification of positive effects, and identification of patients unlikely to benefit from surgery. The purpose of this study was to critically review the present literature to identify the appropriate instruments to assess outcomes in aesthetic surgery. After a comprehensive review of aesthetic surgery outcome instruments, the authors identified body-image and quality-of-life measures to be of the greatest value in determining aesthetic surgery outcomes. These conclusions were based on a critical evaluation of the feasibility, validity, reliability, and sensitivity to change of these measures. The Multidimensional Body-States Relations Questionnaire (MBSRQ), a psychological assessment of body image, was selected as a potential candidate for further study. Two additional body-image assessment instruments, the Facial Appearance Sorting Test (FAST) and the Breast Chest Ratings Scale (BCRS), may be useful in the assessment of rhinoplasty and breast surgery, respectively. The Derriford Scale (DAS59), an instrument that assesses appearance-related quality of life, was also selected. In addition, the authors recommend the use of a generic, utility-based quality-of-life instrument, such as the Health Utilities Index (HUI) or the EuroQol (EQ-5D).
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".