Quality of life outcomes in patients undergoing facial gender affirming surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Facial gender-affirming surgery (FGAS), one of many transition-related surgeries (TRSs), “feminizes” the faces of transgender and gender diverse (TGD) patients undergoing transition. However, it is difficult to demonstrate the medical necessity of FGAS in terms of postoperative quality of life (QoL) outcomes due to a lack of standardized assessment tools. Thus, FGAS remains largely unsubsidized in North America.Methods: A systematic review of online databases was conducted according to PRISMA guidelines. Screening and quality assessment was conducted by two independent blinded reviewers (KJ and GR). For statistical analysis, data from different Likert-scale-like questionnaires were extracted and coalesced into three-point scales on a data table of seven QoL domains; “Pre-” and “Postoperative femininity,” “Psychological satisfaction,” “Social Integration and Functioning,” “Aesthetic Satisfaction,” “Physical Health,” and “Satisfaction with Surgical Results.”Results: From 2000 to 2022, 1837 patients and 3886 procedures from 19 studies were included. Weighted averages across all QoL domains reflected statistically significant improvement compared to neutral following FGAS (p < 0.001). Three studies used the same questionnaire, which showed that out of all eight questions regarding facial appearance, FGAS patients most strongly agreed the surgery was important to their ability to live as a woman (mean = 4.56/5, n = 137). Secondary outcomes showed the most common complications were hardware palpability (3.45%, n = 145) and aberrant scarring (2.17%, n = 423) with an overall revision rate of 2.17% (n = 423). The most common procedure was fronto-orbital remodeling.Conclusion: FGAS significantly improves QoL with minimal risk to life and supports the literature in defining FGAS as a medically necessary procedure comparable to other TRSs.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".