P.036 Are there sex differences in the treatment of myasthenia gravis? a single centre cohort study
Bibliographic record
Abstract
Background: Females with generalized myasthenia gravis (gMG) report lower quality of life (QoL) compared to males. Our objective was to determine whether sex differences in treatment and time to treatment initiation may contribute to this difference. Methods: We performed a single centre retrospective study of people diagnosed with gMG. We used multivariable logistic and Cox regression models to assess the association between sex and study outcomes, adjusting for duration from onset to diagnosis, age at diagnosis, thymoma, and antibody status. Results: 179 people with gMG were included. Mean age at diagnosis was 58.4 years, mean follow-up was 4.8 years, and 58.1% were male. There was no association between sex and odds of starting prednisone (adjusted odds ratio [aOR]=0.58, 95% confidence interval [95%CI]=0.28-1.19, p=0.14) or steroid sparing agents (aOR=0.72, 95%CI=0.39-1.35, p=0.31). Similarly, sex was not associated with time to starting prednisone (adjusted hazard ratio [aHR]=0.74, 95% confidence interval [95%CI]=0.52-1.06, p=0.10) or steroid sparing agents (aHR=0.82, 95%CI=0.55-1.22, p=0.33). Females were more likely to start plasmapheresis (aOR=3.15, 95%CI=1.09-9.07, p=0.03). Conclusions: We found no sex differences in first and second line immunotherapy for gMG that might explain differences in QoL. Females were more likely to initiate plasmapheresis, which may reflect greater disease severity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".