Trajectory of MS disease course for men and women over three eras
Bibliographic record
Abstract
BACKGROUND: Heterogeneity in disease course exists within multiple sclerosis (MS) subtypes. OBJECTIVE: The objective was to estimate disease course heterogeneity over three distinct onset periods (pre-1995, 1995-2004, and 2005-present) for men and women. METHODS: Group-based trajectory model (GBTM) was used to estimate clusters of patients following stable or unstable disease progression trajectories based on the Expanded Disability Status Scale (EDSS). Inception cohorts were generated from the Montreal Neurological Institute MS Clinic registry. Stable trajectories were defined as an EDSS ⩽3.0 and change ⩽1 point over the study period. Annualized relapse rate (ARR) based on the first 5 years of disease was an explanatory variable. RESULTS: Proportion of women classified as stable was 0% for pre-1995, 69.0% for 1995-2004, and 83.9% post-2005; for men, these proportions were 18.4%, 41.4%, and 53.8%, respectively. Men had lower percentage of stable disease than women in both post-1995 cohorts (chi-square p < 0.0001). ARR was associated with higher disability trajectories in both post-1995 cohort (odds ratios >1.0) but not in the pre-1995 cohort. CONCLUSION: Large proportions of patients remain stable at their initial disability level for at least 15 years. Higher ARR increases the odds of patients being in a higher disability trajectory in the latter cohorts.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 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".