Utility of patient subgrouping in ALS clinical trials: a World Federation of Neurology white paper
Bibliographic record
Abstract
The heterogeneity among the amyotrophic lateral sclerosis (ALS)/MND patient population is well recognized but not well understood. Such heterogeneity may represent a significant confound in our current and prior clinical trials as certain subgroups of patients might have a selective response (or resistance) to a novel therapeutic. The basis on which to segregate the patient population is, however, unclear. The ALS/MND Committee of the World Federation of Neurology (WFN) convened a symposium to discuss various strategies that might be considered for separating (stratifying) the population to further study. The results of that conference are presented here as a white paper, reflecting current understanding of several of the various criteria that could be implemented to divide the patient population as presented and discussed at that meeting. Consideration of grouping patients based on phenotype, cognitive involvement, imaging, or electrophysiology is presented here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.717 | 0.728 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.015 | 0.010 |
| 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".