ArticleNature biotechnology2015
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression.
Article in Nature biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
90 citing papers in PubMed, 3 syntheses or guidelines pooled it, 202 citations in OpenAlex.
- Prognostic models for amyotrophic lateral sclerosis: a systematic review.Journal of neurology · 2021Pooled it
- A Systematic and Comprehensive Review on Disease-Causing Genes in Amyotrophic Lateral Sclerosis.Journal of molecular neuroscience : MN · 2020Pooled it
- Mapping of Crowdsourcing in Health: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Trial
- Transplantation of spinal cord-derived neural stem cells for ALS: Analysis of phase 1 and 2 trials.Neurology · 2016Trial
- Development of a machine learning-based survival prediction model for ALS inclusive of the advanced-stage population.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2026Article
- Crowdsourcing and machine learning contests in Parkinson's disease research - when do they work?Journal of Parkinson's disease · 2026Review
- Agreement Between the Harmonized and the Self-Explanatory Versions of the Revised ALS Functional Rating Scale in a Clinical Setting.Muscle & nerve · 2026Article
- Phosphatemia is an Independent Prognostic Factor in Amyotrophic Lateral Sclerosis.Annals of neurology · 2025Article
- Remote Monitoring of Amyotrophic Lateral Sclerosis Using Digital Health Technologies: Shifting Toward Digitalized Care and Research?Neurology · 2025Review
- Predicting Individual Treatment Effects: Challenges and Opportunities for Machine Learning and Artificial Intelligence.Kunstliche intelligenz · 2025Article
- Prognostic Clinical and Biological Markers for Amyotrophic Lateral Sclerosis Disease Progression: Validation and Implications for Clinical Trial Design and Analysis.medRxiv : the preprint server for health sciences · 2024Article
- Disease related changes in ATAC-seq of iPSC-derived motor neuron lines from ALS patients and controls.Nature communications · 2024Article
- The value of routine blood work-up in clinical stratification and prognosis of patients with amyotrophic lateral sclerosis.Journal of neurology · 2024Article
- Venous blood parameters in determination of respiratory impairment in amyotrophic lateral sclerosis.Scientific reports · 2023Article
- Biomarkers: Role and Scope in Neurological Disorders.Neurochemical research · 2023Review
- Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis.BMC medical informatics and decision making · 2023Article
- Towards a Machine Learning Empowered Prognostic Model for Predicting Disease Progression for Amyotrophic Lateral Sclerosis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Deep learning methods to predict amyotrophic lateral sclerosis disease progression.Scientific reports · 2022Article
- Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.Journal of neurology · 2022Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
23 authors at 14 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.