Gender-affirming interventions and prognosis in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Gender-affirming hormone therapy (GAHT) may increase multiple sclerosis (MS) risk, yet MS disease course among transgender and gender-diverse (TGD) individuals remains understudied. We aimed to evaluate the disease course in TGD individuals with MS (TGD-MS). METHODS: Seventeen TGD-MS were identified. Ten TGD-MS on GAHT (five transgender women with MS (TW-MS) and five transgender men with MS (TM-MS)) were investigated further for MS outcomes of symptomatic (clinical)/asymptomatic (radiological) disease activity and progressive MS (PMS) onset. RESULTS: TW-MS (mean = 54.5 years, range = 35.1-66.3) were older than TM-MS (39.2 years, range = 29.6-47.2) at last follow-up. MS onset was later in TW-MS (31.5 years, range = 25.1-39.1) than in TM-MS (21.0 years, range = 15.0-29.8). Three patients (one TW-MS, two TM-MS) experienced symptomatic activity after GAHT initiation (8-10 months); in two patients (one TW-MS, one TM-MS), this marked symptomatic MS onset. Seven patients (four TW-MS, three TM-MS) had ⩾1 clinical/radiological disease activity. Four had PMS; all were TW-MS. In three, GAHT initiation preceded PMS onset (1-5 years). Disability was higher in TW-MS than in TM-MS. CONCLUSION: MS disease course may be modified by GAHT and may differ between TW-MS and TM-MS, highlighting the need for larger long-term studies to overcome disparities in MS care. Comprehensive GAHT history, timely management, and closer follow-up are warranted in TGD-MS.
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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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".