Estimating treatment effect for individuals with progressive multiple sclerosis using deep learning
Bibliographic record
Abstract
Abstract Progressive forms of multiple sclerosis (MS) remain resistant to treatment. Since there are currently no suitable biomarkers to allow for phase 2 trials, pharmaceutical companies must proceed directly to financially risky phase 3 trials, presenting a high barrier to drug development. We address this problem through predictive enrichment, which randomizes individuals predicted to be most responsive in order to increase a study’s power. Specifically, deep learning is used to estimate conditional average treatment effect (CATE) using baseline clinical and imaging features, and rank individuals on the basis of their predicted response to anti-CD20 antibodies. We leverage a large dataset from six different randomized clinical trials (n = 3, 830). In a left-out test set of primary progressive patients from two anti-CD20-antibodies trials, the average treatment effect was significantly greater for the 50% (HR, 0.492; 95% CI, 0.266-0.912; p = 0.0218) and the 30% (HR, 0.361; 95% CI, 0.165-0.79; p = 0.008) predicted to be most responsive, compared to 0.743 (95% CI, 0.482-1.15; p = 0.179) for the entire group. The same model could also identify responders to laquinimod, which has a different mechanism of action. We demonstrate important increases in power that would result from the use of this model for predictive enrichment, enabling short proof-of-concept trials.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".