Sex as a determinant of relapse incidence and progressive course of multiple sclerosis
Bibliographic record
Abstract
The aim of this work was to evaluate sex differences in the incidence of multiple sclerosis relapses; assess the relationship between sex and primary progressive disease course; and compare effects of age and disease duration on relapse incidence. Annualized relapse rates were calculated using the MSBase registry. Patients with incomplete data or <1 year of follow-up were excluded. Patients with primary progressive multiple sclerosis were only included in the sex ratio analysis. Relapse incidences over 40 years of multiple sclerosis or 70 years of age were compared between females and males with Andersen-Gill and Tweedie models. Female-to-male ratios stratified by annual relapse count were evaluated across disease duration and patient age and compared between relapse-onset and primary progressive multiple sclerosis. The study cohort consisted of 11 570 eligible patients with relapse-onset and 881 patients with primary progressive multiple sclerosis. Among the relapse-onset patients (82 552 patient-years), 48,362 relapses were recorded. Relapse frequency was 17.7% higher in females compared with males. Within the initial 5 years, the female-to-male ratio increased from 2.3:1 to 3.3:1 in patients with 0 versus ≥4 relapses per year, respectively. The magnitude of this sex effect increased at longer disease duration and older age (P < 10(-12)). However, the female-to-male ratio in patients with relapse-onset multiple sclerosis and zero relapses in any given year was double that of the patients with primary progressive multiple sclerosis. Patient age was a more important determinant of decline in relapse incidence than disease duration (P < 10(-12)). Females are predisposed to higher relapse activity than males. However, this difference does not explain the markedly lower female-to-male sex ratio in primary progressive multiple sclerosis. Decline in relapse activity over time is more closely related to patient age than disease duration.
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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 | 0.000 |
| 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) | 0.003 | 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".