Identification of prognostic factors predicting the long-term clinical outcome in Multiple Sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS) evolution varies from benign to aggressive forms, and its prognosis remains largely unpredictable, especially in individual cases. Relapse frequency is commonly used as indicator of disease activity and as primary endpoint in randomized clinical trials (RCTs). However, the role of inflammatory attacks on the disease progression is still largely debated. The lack of reliable predictors of the long-term evolution prevents from applying a rational and individualized therapeutic approach. In addition, RCTs methodology is still not sufficiently rigorous for protecting against the bias due to the large variability of the clinical outcome. The project was carried out by analysing the London Ontario (LO) database, one of the largest collections of natural history data from untreated patients, followed up for 28 years. We analysed factors affecting prognosis and predicting disease evolution up to its latest stages. We first investigated in details the relationship between relapses and long-term outcome. The analysis demonstrated poor correlation between number of attacks and the attainment of severe disability, invalidating relapse frequency as surrogate marker for late outcome. In addition, it evidenced the onset of the secondary progressive (SP) phase as the key determinant of prognosis, differentiating patients’ outcome and accounting for the variability of disease course. We therefore analysed in details factors affecting the rate of conversion to SP MS, in order to calculate how the risk of becoming progressive varies with disease duration. This information can be used for designing RCTs using SP onset as primary outcome. We then extensively investigated the effect of age on the disease evolution, before and after the onset of progression. The analysis highlighted age as the strongest determinant of MS prognosis, exerting its predictive effect primarily by affecting the evolution of the relapsing remitting (RR) phase and by increasing the probability of experiencing a progressive course
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".