Nonsurgical Medical Aesthetics and Patient Quality of Life: An Umbrella Review
Bibliographic record
Abstract
Nonsurgical cosmetic facial procedures have become popular treatment options for individuals seeking aesthetic improvements. Despite a breadth of literature on patient satisfaction with treatment outcomes, there is a lack of information specific to changes in quality of life outcomes. The objective of this umbrella review is to report the effectiveness of nonsurgical facial aesthetic treatments on reported quality of life in cosmetic treatment-seeking patients. The authors also aim to identify gaps in the literature on measures of quality of life outside of patient satisfaction. The authors completed a comprehensive, systematic search of review articles across 6 databases, including Medline, CINAHL Plus, EMBASE, APA PsycINFO, Cochrane Reviews, and Google Scholar. They included review-level studies that examine the changes in quality of life measures following treatment. A critical appraisal was completed for each review article included. A total of 7 reviews were included. One review was of strong quality, 2 moderate, and 4 were weak. Several nonsurgical procedures were evaluated across reviews, including injectable neurotoxins, dermal fillers, and laser skin resurfacing. The majority of included studies reported increases in measures of quality of life, posttreatment in the same patient or compared with controls. The most commonly reported measure was psychological well-being, followed by self-perception. There was a lack of measures outside of improvements to aesthetics, including those specific to mental health (eg, depression). Overall, based on the studies on nonsurgical treatments, the authors report increases in overall quality of life. This conclusion should be interpreted with caution, as the majority of reviews included were of moderate-to-weak quality. A major gap in this literature includes mental health outcomes. Future research should focus on increasing the rigor of reporting for systematic reviews.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".