P.049 Determining individual substantial response in amyotrophic lateral sclerosis: utilizing a new method on CENTAUR trial results
Bibliographic record
Abstract
Background: In ALS, determining whether individuals have a substantial response to therapy is a challenge for the field. ALS naturally progresses at variable rates and a personalized approach is required to determine if individuals have a substantial response. A new method to evaluate individual response is proposed and applied to data from the CENTAUR trial of sodium phenylbutyrate/ursodoxicoltaurine (PB/TURSO). Methods: In a post hoc analysis, CENTAUR participants whose actual rate of change from baseline in the ALSFRS-R at week 18 was ≤ their own trial baseline progression rate (ΔFS) were defined as having a substantial individual response in slowing ALS progression. Results: Substantial individual response was observed in a greater proportion of participants receiving PB/TURSO (41%, n=87) vs placebo (19%, n=48; P=0.0076). Conclusions: Response versus ΔFS provides a personalized metric to determine substantial individual response in ALS. ΔFS has been shown to be highly correlated with, but to proportionally underestimate, ALSFRS-R decline in clinical trials. Consequently, those who outperform the ΔFS may be considered to have a substantial individual response. Application to CENTAUR data demonstrates a greater proportion of participants with a substantial individual response in the PB/TURSO arm. These methods may enable greater personalization and analysis of individual response in 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 | 0.083 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".