A multiple sclerosis disease progression measure based on cumulative disability
Bibliographic record
Abstract
BACKGROUND: Existing severity measurements in multiple sclerosis (MS) are often cross-sectional, making longitudinal comparisons of disease course between individuals difficult. OBJECTIVE: The objective of this study is to create a severity metric that can reliably summarize a patient's disease course. METHODS: We developed the nARMSS - normalized ARMSS (age-related MS severity score) over follow-up, using the deviation of individual ARMSS scores from the expected value and integrated over the corresponding time period. The nARMSS scales from -5 to +5; a positive value indicates a more severe disease course for a patient when compared to other patients with similar disease timings. RESULTS: Using Swedish MS registry data, the nARMSS was tested using data at 2 and 4 years of follow-up to predict the most severe quartile during the subsequent period up to 10 years total follow-up. The metric used was area under the curve of the receiver operating characteristic (AUC-ROC). This resulted in measurements of 0.929 and 0.941. In an external Canadian validation cohort, the equivalent AUC-ROCs were 0.901 and 0.908. CONCLUSION: The nARMSS provides a reliable, generalizable and easily measurable metric which makes longitudinal comparison of disease course between individuals feasible.
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.
How this classification was reachedexpand
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".