Gender diversity in adolescents with polycystic ovary syndrome
Bibliographic record
Abstract
OBJECTIVES: The objective of our study was to describe the prevalence of gender diverse (GD) youth among adolescents with polycystic ovary syndrome (PCOS). METHODS: We conducted a retrospective chart review on patients who met NIH criteria for PCOS in our Multidisciplinary Adolescent PCOS Program (MAPP). We compared those with PCOS to MAPP patients who did not meet PCOS criteria as well as to non-PCOS patients from the Adolescent Specialty Clinic (ASC). Variables analyzed included gender identity, androgen levels, hirsutism scores, and mood disorders. We used chi-square, Fisher's exact, t-tests, and Wilcoxon rank sum tests to compare groups. Gender identities self-reported as male, fluid/both or nonbinary were pooled into the GD category. RESULTS: Within the MAPP, 7.6% (n=12) of PCOS youth self-identified as GD compared to 1.8% (n=3) of non PCOS youth (p=0.01, chi-square). When compared to non-PCOS GD adolescents from ASC (4.4%; n=3), the difference to PCOS youth was no longer significant (p=0.56). Among MAPP patients, gender diversity was associated with higher hirsutism scores (p<0.01), but not higher androgen levels. In PCOS, depression/anxiety was higher in GD vs cisgender youth (100% vs. 37.6%, p<0.01 and 77.8% vs. 35.8%, p=0.03 respectively). CONCLUSIONS: Gender diversity was observed more commonly in those meeting PCOS criteria. PCOS GD youth were more hirsute and reported more depression/anxiety. Routine screening for differences in gender identity in comprehensive adolescent PCOS programs could benefit these patients, as alternate treatment approaches may be desired to support a transmasculine identity.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".