Age-Related Differences for Male-to-Female Transgender Patients Undergoing Gender-Affirming Surgery
Bibliographic record
Abstract
INTRODUCTION: It has been theorized that there are 2 subgroups within the male-to-female (MtF) transgender population: individuals who are predominantly androphilic and those who are predominantly gynephylic or interested in both male and female partners. AIM: To explore the role of a dichotomous distribution of age at dysphoria onset in individuals diagnosed with MtF gender dysphoria. METHODS: 40 patients who presented to a surgical clinic in Germany for gender-affirming surgery (GAS) were included in this study. Their age distribution was plotted as a histogram and the population was then divided at the median self-reported age of onset of gender dysphoria-that is, those 17 years and younger and those 18 years and older. The 2 groups were then compared with regard to demographic data, partnership history, various quality of life parameters, as well as sexual orientation and sexual history. MAIN OUTCOME MEASURE: ), Freiburg Personality Inventory, Rosenberg Self-Esteem Scale, and Patient Health Questionnaire were used. RESULTS: Early-onset, gender-dysphoric MtF patients underwent GAS at a much younger age (mean 32.7 vs 43.8 years, P = .004), but had similar characteristics regarding weight, height, body mass index, marital status, and living situation to individuals who reported later onset of gender dysphoria. Preoperatively, they showed greater depressive symptoms (4.6 vs 3.3 points, P = .045), which disappeared after GAS. Following surgery, the younger MtFs were predominantly attracted to men (52.6%), whereas individuals who were diagnosed with late-onset of gender dysphoria preferred women or both men and women (85.7%) as sexual partners (P = .010). Younger trans individuals were more frequently sexually active (73.7% vs 42.9%, P = .049). CONCLUSION: Our findings suggest that there are 2 MtF populations that differ in age of dysphoria onset, sexual history, and multiple personal details including sexual orientation. These data may be used to improve care to transgender individuals by providing treatment reflecting their sexual interests. Zavlin D, Wassersug RJ, Chegireddy V, et al. Age-Related Differences for Male-to-Female Transgender Patients Undergoing Gender-Affirming Surgery. Sex Med 2019;7:86-93.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".