Age and disability accumulation in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVES: We tested the hypothesis that age is a prognostic factor with respect to long-term accumulation of disability in multiple sclerosis (MS). METHODS: Kaplan-Meier analysis and binary logistic regression models determined the effect of age at disease onset, age at onset of progression, and current age on attainment of severe disability levels (Disability Status Scale [DSS] 6-8-10) from the London, Ontario, database (n = 1,023). RESULTS: Older age at relapsing-remitting (RR) phase onset was associated with higher risk of reaching advanced DSS scores. This was independent of disease duration and early relapse frequency but secondary to increased risk of conversion to secondary progressive (SP) MS. Onset at age 40 (odds ratio [OR] = 4.22) and at age 50 (OR = 6.04) doubled and tripled risks of developing SP, compared to age 20 (OR = 2.05). Younger age at conversion to SPMS was associated with shorter times to high DSS scores from disease onset. The progressive course, unaffected by age at RR onset, was only modestly affected by age at SP onset. Among primary progressive and RR/SP patients, median ages at attainment of DSS scores were strikingly similar: DSS = 6, 49 vs 48 years; DSS = 8, 58 vs 58 years; and DSS = 10, 78 years for both (p = NS for all comparisons). CONCLUSIONS: Development of SP is the dominant determinant of long-term prognosis, independent of disease duration and early relapse frequency. Age independently affects disability development primarily by changing probability and latency of SP onset, with little effect on the progressive course.
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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 | 0.000 |
| 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".