Disease-Modifying Therapies in Amyotrophic Lateral Sclerosis: A Network Meta-Analysis of Randomized Clinical Trials
Bibliographic record
Abstract
ABSTRACT Background: Currently, there are no head-to-head studies to compare the efficacy of riluzole, edaravone, sodium phenylbutyrate and taurursodiol (SPT) and tofersen. This study aims to compare all possible interventions for amyotrophic lateral sclerosis (ALS) using network meta-analysis (NMA) methods. Methods: A conventional meta-analysis was done if at least two studies with the same intervention, control and outcomes were present. Since all studies included were randomized clinical trials, a NMA comparing five interventions was done, especially when similarity and consistency were assured. Both riluzole and edaravone had three clinical trials included, while SPT and tofersen each had one. Results: A total of 1601 ALS patients were included in this review, 1185 in the intervention group and 416 in the control group. Compared to placebo, ALS patients taking riluzole had 36% higher probability of surviving (OR: 1.36, I 2 = 4%, p = 0.03, FEML) while those in the edaravone group had 1.44 point lower ALSFRS-R score (SMD: 1.44, p = 0.19, I 2 = 98%, REML) at study end. Comparing all interventions in terms of mortality, all no interventions were significantly different to placebo. Moreover, compared to one another, no statistically significant differences were noted. Conclusion: Despite the benefit of riluzole in terms of survival in conventional meta-analysis, non-significant findings and the lack of comparison of ALSFRS-R to placebo, edaravone, SPT and tofersen in NMA may preclude any strong recommendation for its use. Moreover, the difference in outcome measures limits important comparison between interventions, and while global consistency in NMA was satisfied, the heterogeneity of patient population limits the interpretability of our results.
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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 | 0.035 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".