ArticleFrontiers in neurology2021
Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease.
Article in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed, 25 citations in OpenAlex.
- Modeling Disease Progression and Placebo Response in Huntington Disease: Insights From Enroll-HD and GENERATION HD1 Cohorts.Neurology · 2025Trial
- A Two-Track Model of Huntington's Disease Pathology: Striatal Atrophy Mediates Maladaptive Immune Dysregulation.International journal of molecular sciences · 2026Article
- Antidopaminergic Medications and Clinical Changes in Measures of Huntington's Disease: A Causal Analysis.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- The Safety Profile of Pridopidine, a Novel Sigma-1 Receptor Agonist for the Treatment of Huntington's Disease.CNS drugs · 2025Article
- Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis and driving capability prediction.Orphanet journal of rare diseases · 2023Observational
- Lumbar puncture safety and tolerability in premanifest and manifest Huntington's disease: a multi-analysis cross-sectional study.Scientific reports · 2022Article
- Self-Reported Social Relationship Capacities Predict Motor, Functional and Cognitive Decline in Huntington's Disease.Journal of personalized medicine · 2022Article
- Clustering and prediction of disease progression trajectories in Huntington's disease: An analysis of Enroll-HD data using a machine learning approach.Frontiers in neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Huntington's disease (HD) is characterised by a triad of cognitive, behavioural, and motor symptoms which lead to functional decline and loss of independence. With potential disease-modifying therapies in development, there is interest in accurately measuring HD progression and characterising prognostic variables to improve efficiency of clinical trials. Using the large, prospective Enroll-HD cohort, we investigated the relative contribution and ranking of potential prognostic variables in patients with manifest HD. A random forest regression model was trained to predict change of clinical outcomes based on the variables, which were ranked based on their contribution to the prediction. The highest-ranked variables included novel predictors of progression-being accompanied at clinical visit, cognitive impairment, age at diagnosis and tetrabenazine or antipsychotics use-in addition to established predictors, cytosine adenine guanine (CAG) repeat length and CAG-age product. The novel prognostic variables improved the ability of the model to predict clinical outcomes and may be candidates for statistical control in HD clinical studies.
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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.