Clinical Trials in ALS – Current Challenges and Strategies for Future Directions
Bibliographic record
Abstract
Historically, clinical trials in ALS have a poor track record, with only two therapies having been approved in Canada and the United States to date: riluzole (Rilutek®) and edaravone (Radicava®). The modest efficacy profiles of riluzole and edaravone highlight the need to continue to search for novel therapies for this devastating disease. Conventional drug development, in which the largest variable is whether or not the investigational product is effective, is dealt an additional layer of complexity in the case of ALS, as ALS is a poorly understood disease. Clinical trials in ALS have suffered from disease heterogeneity that is difficult to control for, a lack of established biomarkers, flawed outcome measures, poor trial design, patient recruitment and retention challenges, and regulatory nuances. However, from each failed therapeutic program, of which there have been over 40, lessons have been learned that are paving the way for future ALS trials. Novel biomarkers and outcome measures are bettering our understanding of disease progression, and randomization stratification, as well as predictive models, are being used to counter heterogeneity. The field is employing more innovative trial designs that have proved to be successful in other disease areas in order to increase the speed at which trials are conducted and decrease associated costs. Together, these new initiatives will provide the best chance of success to potential therapies for ALS.
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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 | 0.301 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.021 | 0.048 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.025 | 0.035 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".