Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging
Bibliographic record
Abstract
BACKGROUND: New multiple sclerosis (MS) lesion activity on magnetic resonance imaging (MRI) can test immunomodulatory therapies in proof-of-concept trials. Comparably powerful endpoints to assess tissue protection or repair are lacking. OBJECTIVE: The objective of this paper is to report sample-size calculations for assessment of new lesion recovery. METHODS: In two sets of six active MS cases, new lesions were observed by monthly MRI for approximately 12 months. Averages and quartiles of normalized (proton density/T1/T2 weighted) and quantitative (T1/T2 and mean diffusivity maps for dataset 1, T2 and magnetization transfer ratio maps for dataset 2) measures were used to compare the lesion area before lesion appearance to afterward. A linear mixed-effects model incorporating lesion- and participant-specific random effects estimated average levels and variance components for sample-size calculations. RESULTS: In both datasets, greatest statistical sensitivity was observed for the 25th percentile of normalized proton density-weighted signal. At 3T, using new lesions ⩾15 mm(3), as few as nine participants/arm may be required for a six-month placebo-controlled add-on trial postulating a therapeutic effect size of 20% and statistical power of 90%. CONCLUSION: Lesion recovery is a powerful outcome measure for proof-of-concept clinical trials of tissue protection and repair in MS. The trial design requires active cases and is therefore best implemented near disease onset.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".