Is the Symbol Digit Modalities Test a useful outcome in secondary progressive multiple sclerosis?
Bibliographic record
Abstract
BACKGROUND: It is unclear which cognitive outcome measure is the most useful for clinical trials in multiple sclerosis. To investigate the usefulness of the Symbol Digit Modalities Test (SDMT) as a clinical outcome measure in secondary progressive multiple sclerosis (SPMS), we describe the frequency of worsening and improvement events in a large randomized controlled trial (RCT) dataset. METHODS: Using original trial data from the ASCEND trial (n = 889), a recent large RCT in SPMS, we describe worsening and similarly defined improvement with and without 3-month confirmation on the SDMT in the whole trial cohort and unconfirmed worsening and improvement on the Paced Auditory Serial Addition Test (PASAT) in a smaller subset (n = 107). RESULTS: Somewhat unexpectedly, SDMT scores steadily increased throughout the 2 years of follow-up in this trial. There were overall few SDMT worsening events throughout the trial (generally fewer than 10% of participants), but improvement events steadily increased from around 50% of participants with improvement at 12 weeks to more than 70% at 84 weeks and beyond. PASAT scores followed a similar pattern. CONCLUSIONS: In this well-characterized clinical trial cohort, the SDMT does not reflect the steady cognitive decline that patients with SPMS experience. Both SDMT and PASAT scores improve throughout follow-up, possibly due to a practice effect. The SDMT may not be a useful outcome measure of disease progression in 2-year 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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".