An enrichment strategy for clinical trials in SPMS
Bibliographic record
Abstract
BACKGROUND: We recently compared clinical outcomes in secondary progressive MS (SPMS) clinical trials and found an association of timed 25 foot walk (T25FW) worsening events and baseline disability scores. It is unclear whether disability worsening in clinical trials is comparable to that seen in clinical practice. OBJECTIVE: The objective of this study is to compare disability worsening between the IMPACT and ASCEND data sets and data from the Calgary MS clinic and to characterize the association of baseline T25FW and expanded disability status scale (EDSS) scores with disability worsening. METHODS: We combined the three data sets and investigated the impact of baseline characteristics on disability worsening with a logistic regression model. We calculated T25FW, EDSS, and 'EDSS or T25FW' worsening events as a function of ascending cut-off baseline disability scores. RESULTS: Data source was not associated with T25FW worsening at 12 months. There was a strong association of baseline T25FW and EDSS cut-off scores with T25FW worsening. No such association was present for the EDSS and 'EDSS or T25FW'. CONCLUSION: Our results suggest that it is possible to 'enrich' a trial cohort for expected T25FW worsening events using specific baseline T25FW and EDSS cut-off scores. These analyses inform the selection of 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 | 0.309 | 0.600 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".