Clinical outcome measures in SPMS trials: An analysis of the IMPACT and ASCEND original trial data sets
Bibliographic record
Abstract
BACKGROUND: Still too little is known about the natural history of clinical outcome measures beyond the Expanded Disability Status Scale (EDSS), such as the timed 25-foot walk (T25FW) and nine-hole peg test (9HPT) in secondary progressive multiple sclerosis (SPMS). OBJECTIVE: To describe progression on the EDSS, T25FW, 9HPT, and their combinations. To investigate the association of the baseline characteristics age, sex, EDSS, T25FW, gadolinium-enhancing lesions, and relapse activity with EDSS and T25FW progression. METHODS: Using original trial data from the placebo arms of the IMPACT and ASCEND randomized controlled trials, we describe disability progression (with and without 3- or 6-month confirmation). We investigated the association of selected baseline characteristics with EDSS and T25FW progression over 2 years using binary logistic regression. RESULTS: T25FW was the single outcome measure with the largest proportion of patients progressing, followed by EDSS and 9HPT. EDSS and T25FW at baseline were associated with EDSS and T25FW progression in both data sets. Age and relapse activity were only mild and inconsistent predictors, while sex and gadolinium enhancement at baseline did not predict disability progression in either data set. CONCLUSION: Our analyses inform the selection of primary outcome measures as well as inclusion criteria for clinical trials in SPMS.
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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 | 0.135 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".