Transgender and other gender diverse adolescents with eating disorders requiring medical stabilization
Bibliographic record
Abstract
BACKGROUND: Despite the high prevalence of eating disorders in gender diverse adolescents, little is known about the characteristics of gender diverse youth with eating disorders who require inpatient medical stabilization. The primary objective of this study was to describe the medical, anthropometric, and psychiatric characteristics of gender diverse adolescents hospitalized for eating disorders and compare these characteristics to cisgender peers hospitalized for eating disorders. The secondary objective was to evaluate percent median body mass index as one marker of malnutrition and treatment goal body mass index as a recovery metric between patients' birth-assigned sex and affirmed gender using standardized clinical growth charts. METHODS: A retrospective chart review was conducted of 463 patients admitted to an inpatient eating disorders medical unit between 2012 and 2020. To compare medical, anthropometric, and psychiatric data between gender diverse and cisgender patients, chi-square/Fisher's exact and t-tests were used. Clinical growth charts matching the patients' birth-assigned sex and affirmed gender identity were used to assess percent of median body mass index and treatment goal body mass index. RESULTS: Ten patients (2.2%) identified as gender diverse and were younger than cisgender patients [13.6 (1.5) years vs. 15.6 (2.7) years, p = 0.017]. Gender diverse patients were hospitalized with a higher percent median body mass index compared to cisgender peers [97.1% (14.8) vs. 87.9% (13.7), p = 0.037], yet demonstrated equally severe vital sign instability such as bradycardia [44 (8.8) beats per minute vs. 46 (10.6) beats per minute, p = 0.501], systolic hypotension [84 (7.1) mmHg vs. 84 (9.7) mmHg, p = 0.995], and diastolic hypotension [46 (5.8) mmHg vs. 45 (7.3) mmHg, p = 0.884]. Gender diverse patients had a higher prevalence of reported anxiety symptoms compared to cisgender patients (60% vs. 28%, p = 0.037). CONCLUSIONS: Gender diverse patients demonstrated complications of malnutrition including vital sign instability despite presenting with a higher weight. This is consistent with a greater proportion of gender diverse patients diagnosed with atypical anorexia nervosa compared to cisgender peers. Additionally, psychiatric comorbidities were present among both groups, with a larger percentage of gender diverse patients endorsing anxiety compared to cisgender patients.
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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 | 0.000 |
| 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".