The natural history of secondary progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The onset of secondary progression is a pivotal event in the course of relapsing-remitting (RR) multiple sclerosis (MS). Patients with secondary progressive MS (SPMS) experience continuous worsening of symptoms independent of the occurrence of relapses. Possible risk factors associated with the onset of SPMS remain under investigated in natural history studies of MS disease course. METHODS: We used Kaplan-Meier survival analyses and Cox regression models to investigate the influence of gender, onset age and onset symptoms on time to and age at SPMS in British Columbia (BC) MS patients with a RR disease onset who were not exposed to immunomodulatory drugs. RESULTS: Of 5778 patients in the BCMS database with definite MS, 5207 (90%) had an RR onset. Median time to SPMS was 21.4 years (95% CI 20.6 to 22.2), reached at a median age of 53.7 years (95% CI 53.1 to 54.3). Male gender and motor onset symptoms were associated with a shorter time to and a younger age at SPMS. A younger age at disease onset was associated with a longer time to SPMS but also with a younger age at secondary progression. Other onset symptoms were not associated with time to, or age at, SPMS. CONCLUSIONS: We identified three factors influencing the onset of SPMS in untreated patients with RRMS: motor onset symptoms and male gender were associated with both a shorter time to and a younger age at SPMS. A younger age at disease onset should not be viewed as indicating a better prognosis.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".