Scoring treatment response in patients with relapsing multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: We employed clinical and magnetic resonance imaging (MRI) measures in combination, to assess patient responses to interferon in multiple sclerosis. OBJECTIVE: To optimize and validate a scoring system able to discriminate responses to interferon treatment in patients with relapsing-remitting multiple sclerosis (RRMS). METHODS: Our analysis included two large, independent datasets of RRMS patients who were treated with interferons that included 4-year follow-up data. The first dataset ("training set") comprised of 373 RRMS patients from a randomized clinical trial of subcutaneous interferon beta-1a. The second ("validation set") included an observational cohort of 222 RRMS patients treated with different interferons. The new scoring system, a modified version of that previously proposed by Rio et al., was first tested on the training set, then validated using the validation set. The association between disability progression and risk group, as defined by the score, was evaluated by Kaplan Meier survival curves and Cox regression, and quantified by hazard ratios (HRs). RESULTS: The score (0-3) was based on the number of new T2 lesions (>5) and clinical relapses (0,1 or 2) during the first year of therapy. The risk of disability progression increased with higher scores. In the validation set, patients with score of 0 showed a 3-year progression probability of 24%, while those with a score of 1 increased to 33% (HR = 1.56; p = 0.13), and those with score greater than or equal to 2 increased to 65% (HR = 4.60; p < 0.001). CONCLUSIONS: We report development of a simple, quantitative and complementary tool for predicting responses in interferon-treated patients that could help clinicians make treatment decisions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".