Regression of new gadolinium enhancing lesion activity in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Contrast enhancing lesions (CEL) is a common endpoint in multiple sclerosis (MS) clinical trials. To minimize sample size or placebo exposure, a crossover design without a concurrent control group is attractive. Natural regression may confound this strategy. We assessed the degree of regression in monthly new gadolinium activity in relapsing-remitting (RR) placebo patients. METHODS: A post hoc analysis was performed on 65 RRMS placebo patients in the Prevention of Relapses and disability by Interferon beta-1a Subcutaneously in Multiple Sclerosis (PRISMS) trial. Patients were originally selected for relapses but not preselected for MRI activity. Eleven MRI scans were taken at screening, baseline, and months 1 through 9. Monthly new CEL rates were examined using a random effects Poisson model. Patients were analyzed as a single group and by screening CEL count level subgroups: no, low, and high (0, 1 to 3, >3 CEL). RESULTS: A total of 32, 19, and 14 patients had no, low, and high CEL counts at screening. The monthly new CEL rates (95% CI) of all patients at baseline, months 1 to 3, 4 to 6, and 7 to 9 were 2.0 (1.3, 2.9), 1.8 (1.3, 2.5), 1.4 (1.0, 2.0), and 1.2 (0.8, 1.7). Compared to baseline, the rate decreased by 10%, 27%, and 39%. The monthly rate of the no subgroup remained stable. The rates for both the low and high subgroups decreased by 4%, 29%, and 48% at months 1 to 3, 4 to 6, and 7 to 9 compared to baseline. CONCLUSIONS: Placebo relapsing-remitting multiple sclerosis patients experience a decline of new gadolinium activity over 9 months. A crossover design without a concurrent comparison group may overestimate the treatment effect.
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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 | 0.009 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".