ArticleMovement disorders clinical practice2026
Closing the Gate Before the Horse is out of the Barn: A Model to Effectively Predict the First Fall in Patients with Parkinson's Disease.
Article in Movement disorders clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A machine learning approach to quantifying fall conversion risk in fall-naïve Parkinson's patients.Parkinsonism & related disorders · 2026Article
- A Comprehensive Evaluation of Mobility: Validation of the Functional Ambulation and Stair Test in Older Adults.Journal of clinical medicine · 2026Article
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.
Funding
Abstract
backgroundMost fall-prediction models in Parkinson's disease (PD) rely on prior falls, limiting prevention. The Waiting Room of the Future (WROTF) platform collects standardized motor and cognitive measures during routine visits, integrated into electronic health records (EHR).
objectiveTo develop a first-fall prediction model combining WROTF-EHR data and compare its performance with established cognitive- and motor-based models.
methodsPreviously published motor (Paul) and cognitive (CYCLE-II) models were refit using logistic regression. A new regularized (LASSO) model was developed. All models were evaluated using all participants and only those without prior falls.
resultsAmong 997 PD patients (mean age 69.6 ± 8.8; 62.8% male; disease duration 3.6 ± 4.3 years), AUCs were 0.77 (Paul), 0.70 (CYCLE-II), and 0.81 (LASSO). In fall-naïve patients, AUCs were 0.62, 0.68, and 0.70. Key predictors included processing speed, walking speed, disease duration, and age.
conclusionsThe model enables early fall-risk detection using routinely collected data, supporting proactive and personalized preventive care.
Indexed as
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.