Personalized activity index, a new safety monitoring tool for multiple sclerosis clinical trials
Bibliographic record
Abstract
BACKGROUND: An abnormal increase of contrast-enhancing lesion (CEL) counts on frequent MRIs is interpreted as a signal of potential worsening in multiple sclerosis (MS) clinical trials. We demonstrate the utility of the MR personalized activity index (MR-pax) to identify such increases. METHODS: = 167) with MRIs at screening, baseline and months 1-6. We performed five consecutive reviews at 90-day intervals. At each review, we evaluate the MR-pax for each patient and also identify those who meet the rule-of-five (an ad-hoc guideline currently in use). To evaluate its clinical relevance, we assess the relation between having a small MR-pax (≤0.05; indicating an unexpected CEL increase) and relapse status in the 12 weeks post-review. RESULTS: Of the 399 patient reviews, 35 cases met the rule-of-five; 35 had an MR-pax ≤ 0.05; 18 met both criteria. The proportions experiencing clinical relapse are 63% among those meeting the rule-of-five, 61% among those with MR-pax ≤0.05, and 83% for those meeting both criteria, more than double the rate of those meeting neither criterion (40%). CONCLUSION: A guideline combining this new personalized index and the existing threshold-based criterion is able to better identify patients with a higher risk of experiencing relapses.
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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 | 0.089 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".