Comprehensive analysis platform to understand, remedy, and eliminate amyotrophic lateral sclerosis (CAPTURE ALS): Study protocol for a Canadian multicenter, multimodal, longitudinal observational study
Bibliographic record
Abstract
BACKGROUND: The marked heterogeneity of Amyotrophic Lateral Sclerosis (ALS) combined with a lack of biomarkers are key contributing factors to the lack of disease-modifying treatments. The Comprehensive Analysis Platform to Understand Remedy and Eliminate ALS (CAPTURE ALS) is a Canadian platform designed to create the most comprehensive picture of people living with ALS with the objective of facilitating ALS research initiatives worldwide. OBJECTIVES: The main aims of CAPTURE ALS include: (1) to characterize ALS and healthy controls with biosamples and data in order to provide the most comprehensive picture of individuals living with ALS to date; (2) to create a de-identified database and biosample repository linked to detailed clinical information; and (3) to develop and implement an inclusive and transparent participant engagement strategy to be active throughout all stages of CAPTURE ALS. METHODS/RESULTS: CAPTURE ALS is a prospective, multicenter, observational, longitudinal study. People living with ALS, or a related disease and healthy controls undergo a harmonized protocol including the collection of detailed clinical information, neurological and cognitive examination, speech recording, advanced magnetic resonance imaging, and biosampling. Data and samples are stored in a biobank operating under an open science governance framework. An inclusive and transparent participant engagement strategy was designed and implemented throughout all stages of CAPTURE ALS. Four sites are operating in the consortium with a fifth being onboarded. The target enrollment is 120 affected participants and 50 controls, with the first participant visit having occurred in March 2022. Recruitment is ongoing. DISCUSSION: CAPTURE ALS is a scalable clinical research platform that connects scientists and patients to facilitate efficient translational research. The unique and deeply phenotyped data and biosamples are a global resource towards the development of biomarkers and understanding ALS biology. This study is registered at clinicaltrials.gov (NCT: NCT05204017).
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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 | 0.049 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".