Does MRI lesion activity regress in secondary progressive multiple sclerosis?
Bibliographic record
Abstract
BACKGROUND: The rate of new contrast-enhancing lesions (CELs) on monthly magnetic resonance imaging (MRI) scans has been shown to decrease over a 9-month period in placebo-treated patients with relapsing-remitting (RR) multiple sclerosis (RRMS). OBJECTIVE: We examined this phenomenon in placebo-treated secondary progressive MS (SPMS) patients. METHODS: Patients were chosen from two clinical trials. Monthly scans were taken at screening, baseline and months 1-9 for Cohort-1 and months 1-6 for Cohort-2. We examined the monthly new CEL rates according to initial CEL level: 0, 1-3, >3 CELs at screening, and presence and absence of pre-study relapses. RESULTS: Respectively, 59, 21 and 14 of the 94 Cohort-1 patients, and 36, 17 and 9 of the 62 Cohort-2 patients had 0, 1-3 and >3 initial CELs. For Cohort-1, the monthly new CEL rates did not change during follow-up, regardless of initial CEL level. For Cohort-2, the monthly rate was unchanged in the 0 initial CEL subgroup, but decreased 33% (95% confidence interval: 8%, 52%) from months 1-3 to months 4-6 in the other two subgroups. For the combined cohorts, a decreasing rate was observed in the 12 patients with >3 initial CELs and pre-study relapses. CONCLUSIONS: The short-term trend of new CEL activity in placebo-treated SPMS patients may vary across cohorts.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".