Head-to-head drug comparisons in multiple sclerosis
Bibliographic record
Abstract
Disease-modifying drugs are changing the natural history of multiple sclerosis (MS). However, currently available clinical trial data are insufficient to develop accurate personalized treatment algorithms to assign the best possible treatment to each person with MS according to disease features, treatment history, and comorbidities. Such accurate algorithms would require the presence of numerous head-to-head trials of long duration, which is virtually impossible, given the economic costs, required time, and difficulties with attrition. Thus, efforts are being made to compare relative treatment efficacy through observational designs, using large multicenter prospective cohorts or "big MS data," and network meta-analyses. Although such studies can yield useful information, they are liable to biases and their results should be confirmed in other study populations, including smaller, single-center cohorts, where some of these biases can be minimized. In this View article, we analyze the potential benefits and biases of all these strategies alternative to head-to-head trials in MS. Finally, we propose the combination of all these types of studies to obtain reliable head-to-head drug comparisons in the absence of randomized designs.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".