The Prognostic Value of Initial Relapses on the Evolution of Disability in Patients with Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
The evolution of multiple sclerosis (MS) and the resulting disability are unpredictable. To identify clinical variables that could be potential prognostic factors, we followed a cohort of 288 patients diagnosed as having relapsing-remitting MS between 1990 and 2003. The end point was the first occurrence of a non-reversible EDMUS-GS score >or=3 (moderate disability). The impact of the number of MS attacks during the first 2 years of the disease as well as the first interattack interval were assessed in two Cox models, one using a fixed-in-time covariate, the other using a time-dependent covariate. Older age at onset and a higher number of MS attacks during the first 2 years of MS proved to be predictors of unfavourable prognosis. The first interattack interval had no influence on the evolution of the disability, conversely to the first relapse which had a short-term impact on the prognosis. We confirmed that the age at onset and the number of MS attacks during the first 2 years of MS are predictors of the evolution of the disability and demonstrated the importance of using time-dependent covariates.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".