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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".