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 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 | 0.187 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".