Enhancing clinical trials in neurodegenerative disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review article is focused on strategies that may enhance clinical trial efficiency in neurodegenerative disorders, as demonstrated within the research field of amyotrophic lateral sclerosis (ALS). RECENT FINDINGS: Unravelling ALS pathophysiology will result in an increased number of candidate therapeutics. Recent ALS clinical trials have employed novel study designs that expedite the drug development process and limit sample size, including futility, lead-in, selection, adaptive and sequential designs. The search for sensitive and specific biomarkers in ALS continues to develop, and they are essential in accelerating the drug discovery process. Several candidate cerebrospinal fluid (CSF), neuroimaging and electrophysiological biomarkers have been recently described in ALS, and some have been successfully employed as secondary outcome measures in clinical trials. The advent of web-based technologies has provided a complementary platform to expedite clinical trials, through electronic data capture, teleconferencing and online registries. In addition, the formation of ALS consortia has enhanced collaborative multicentre studies. SUMMARY: ALS research studies have employed novel strategies to accelerate the efficiency and pace of drug discovery. The importance of adapting to novel measures that enhance study efficiency is not unique to ALS and can be applied to other neurodegenerative diseases in search of effective treatments.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".