Short-term correlations between clinical and MR imaging findings in relapsing-remitting multiple sclerosis.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Despite extensive use of MR imaging to provide markers of multiple sclerosis (MS) activity and accumulated disease burden, the magnitude of the relationship between clinical and MR findings is still debated. Using data from the European/Canadian glatiramer acetate (GA) trial, we investigated short-term correlations between clinical and MR measures of disease activity in patients with relapsing-remitting MS (RRMS). METHODS: In a 9-month, double-blinded, placebo-controlled study, 239 patients with RRMS were randomly assigned to receive 20 mg GA (n = 119) or placebo (n = 120). Clinical assessment included monthly neurologic examinations with Expanded Disability Status Scale scoring and visits for symptoms suggestive of relapse. Dual-echo T2-weighted and pre- and postcontrast T1-weighted brain MR images were obtained at baseline and monthly during follow-up. Contrast-enhancing and new T2-hyperintense lesions were counted, and total T2-hyperintense and T1-hypointense lesion volumes were measured. RESULTS: Significant univariate correlations were found between the number of relapses during the study period and the number of enhancing lesions at baseline (r = 0.25) and during follow-up (r = 0.30) in the study population as a whole. Multivariable analysis showed that two independent factors were more strongly correlated with relapse frequency: the number of relapses during the 2 years before entry and the number of on-trial enhancing lesions, in the whole study population and in the placebo group. CONCLUSION: In RRMS, MR imaging measures of inflammatory activity are modestly but significantly correlated with the occurrence of clinical attacks over the short term. Clinical and MR imaging assessment can provide complementary outcome measures for RRMS trials.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".