ArticleNPJ Parkinson's disease2025
Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson's disease.
Article in NPJ Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this uncontrolled, open-label exploratory clinical study, the authors explore the potential of blink data as a digital biomarker for estimating clinical indices of Parkinson's disease (PD) using a machine learning approach. Blink data were collected from 20 patients with PD before and after (up to 4 h) L-dopa/decarboxylase inhibitor administration. Concurrent assessments of patient diary-based ON/OFF and dyskinesia, L-dopa plasma concentration, and MDS-UPDRS Part III scores were conducted at 30 min intervals. The models were developed to predict clinical symptoms based on blink data collected at 3 min intervals. The most effective post-processing models accurately predicted the ON/OFF states (mean area under the receiver operating characteristic curve (AUC
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.