Outcomes in Facial Feminization Surgery: A Systematic Review
Bibliographic record
Abstract
Objective: Review literature on facial feminization surgery (FFS) for the transgender population and identify whether heterogeneity in reported outcomes and outcome measures exists across studies, as measured by a lack of consensus, and number of outcomes and outcome measures used. Evidence Review: A search of MEDLINE and EMBASE (database inception to January 20, 2021) was performed to retrieve FFS studies. Primary outcomes included number of reported outcomes and outcome measures; secondary outcomes included clinimetric properties of outcome measures and study characteristics. Findings: In total, 15 articles were included. Sixty-nine outcomes and 12 outcome measures were identified. Of those outcome measures, zero were found to be valid, reliable, and responsive in patients who had undergone FFS. A variety of FFS interventions were studied, with the three most common interventions being: rhinoplasty ( n = 7, 46.7%), mandibuloplasty ( n = 7, 46.7%), and chondrolaryngoplasty ( n = 6, 40%). Conclusion and Relevance: Heterogeneity was evident in reported outcomes and outcome measures in FFS literature and there is currently no outcome measure commonly used for this patient population.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".