No sex‐based differences in odds of starting or time to treatment of generalized myasthenia gravis: A single center cohort study
Bibliographic record
Abstract
INTRODUCTION/AIMS: Females with generalized myasthenia gravis (gMG) report lower quality of life (QoL) and have more severe disease than males. Sex differences in disease characteristics exist, however whether there are sex differences in the treatment of gMG that may contribute to QoL disparities is unknown. Our objective is to determine whether there are sex differences in the treatment of gMG. METHODS: We performed a single-center retrospective study of people diagnosed with gMG at the University of Calgary between 1997 and 2021. Primary outcome was proportion starting treatment and secondary outcome was time from diagnosis to treatment initiation. Treatments included pyridostigmine, prednisone, steroid sparing therapies (azathioprine, mycophenolate mofetil [MMF], methotrexate [MTX], or tacrolimus), intravenous immunoglobulin (IVIg), plasmapheresis, rituximab, eculizumab, cyclosporine, stem cell transplantation, and thymectomy. Multivariable logistic and Cox proportional hazards regression models were used to examine treatment associations with sex, adjusted for time from onset to diagnosis, age at diagnosis, presence of thymoma, and antibody status. RESULTS: A total of 179 people with gMG were included (41.9% female). Odds of starting treatment were not statistically associated with sex after adjustment for confounders and correction for multiple testing. Results of the secondary analysis using time to treatment initiation as the outcome were similar. DISCUSSION: We found no sex differences in odds of starting treatment or time to treatment initiation that might explain previously observed sex-based differences in QoL. Future work should capture physician and patient treatment preferences that may influence disease management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 teacher head, 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".