Risk of secondary progressive multiple sclerosis: A longitudinal study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The risk factors for conversion from relapsing-remitting to secondary progressive multiple sclerosis remain highly contested. Objective: The aim of this study was to determine the demographic, clinical and paraclinical features that influence the risk of conversion to secondary progressive multiple sclerosis. Methods: Patients with adult-onset relapsing–remitting multiple sclerosis and at least four recorded disability scores were selected from MSBase, a global observational cohort. The risk of conversion to objectively defined secondary progressive multiple sclerosis was evaluated at multiple time points per patient using multivariable marginal Cox regression models. Sensitivity analyses were performed. Results: A total of 15,717 patients were included in the primary analysis. Older age (hazard ratio (HR) = 1.02, p < 0.001), longer disease duration (HR = 1.01, p = 0.038), a higher Expanded Disability Status Scale score (HR = 1.30, p < 0.001), more rapid disability trajectory (HR = 2.82, p < 0.001) and greater number of relapses in the previous year (HR = 1.07, p = 0.010) were independently associated with an increased risk of secondary progressive multiple sclerosis. Improving disability (HR = 0.62, p = 0.039) and disease-modifying therapy exposure (HR = 0.71, p = 0.007) were associated with a lower risk. Recent cerebral magnetic resonance imaging activity, evidence of spinal cord lesions and oligoclonal bands in the cerebrospinal fluid were not associated with the risk of conversion. Conclusion: Risk of secondary progressive multiple sclerosis increases with age, duration of illness and worsening disability and decreases with improving disability. Therapy may delay the onset of secondary progression.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it