Hospitalizations as an outcome measure in COURAGE-ALS
Bibliographic record
Abstract
Objective: To describe the development of a methodology to characterize hospitalizations and their relationship to amyotrophic lateral sclerosis (ALS) and provide results using this process in a phase 3 trial of reldesemtiv in ALS. Methods: ALS clinical trialists assisted in developing a classification system to determine if a hospitalization was related to ALS (HR-ALS), unrelated (HU-ALS), or if the relationship was indeterminate (HI-ALS) and this was applied by the investigators to hospitalizations in COURAGE-ALS. Time to first hospitalization and number of hospitalizations were compared between those assigned reldesemtiv or placebo for up to 48 weeks. Demographic and clinical features were evaluated for prediction of hospitalization risk; this analysis was limited to those participants who completed the first 24-week double-blind placebo-controlled portion of the trial. Results: COURAGE-ALS terminated early due to futility. Time to first hospitalization was similar in the reldesemtiv compared to placebo arms as was the incidence, with 86 of the participants (17.6% of those originally assigned placebo and 18.0% originally on reldesemtiv) experiencing an event. The largest percentage of events was classified as HR-ALS for both placebo (64%, 18/28) and reldesemtiv (76%, 44/58). In a multivariate model, only bulbar or respiratory onset disease was a significant risk factor for hospitalization. Conclusion: While most hospitalizations in COURAGE-ALS were HR-ALS, HU-ALS and HI-ALS also occurred. When using hospitalization as an endpoint in an ALS clinical trial, recording its relationship to ALS provides additional details to characterize disease burden and clinical meaningfulness of the endpoint.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".