Toward a Unified Definition of Progression Independent of Relapse Activity in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVES: Progression independent of relapse activity (PIRA) is the main driver of disability accumulation in relapsing multiple sclerosis (MS). We tested various PIRA definitions against the risk of long-term disability. METHODS: Patients with relapsing MS, first visit ≥January 1, 2000, ≥3 visits with Expanded Disability Status Scale (EDSS), and ≥5-year follow-up were extracted from the Italian MS and Related Disorders Register on September 29, 2023. Eighteen PIRA definitions were obtained combining fixed or roving baseline, 24-week, 48-week confirmed or sustained disability accrual, no relapses ≤90 days before/≤30 days after the event (90d), ≤180 days before/≤30 days after the event, or absence of relapses from baseline to confirmation score (ABS). Predictive performance against the reaching of EDSS = 6.0 was calculated. RESULTS: A total of 30,203 patients were included. After a follow-up of 11.3 ± 4.3 years, PIRA ranged from 38.8% to 74.1%. EDSS = 6.0 was detected in 4,401 (15%) patients. Sensitivity of PIRA definitions against EDSS = 6.0 was higher using the 90d criterion (66.7%-69.4%), while the ABS criterion increased specificity (55.3%-62.2%). DISCUSSION: The definition of PIRA combining roving baseline, no relapses 90 days before and 30 days after the event and 24-week confirmation achieved the best predictive value and feasibility, supporting its use in routine practice and research.
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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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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 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".