ArticleSleep2024
The pros and cons of using automated sleep scoring in sleep research.
Article in Sleep, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- World of ScoreCraft: Novel Multi-Scorer Experiment on the Impact of a Decision Support System in Sleep Staging.Journal of sleep research · 2026Article
- REST-a deep learning tool for automated mouse sleep stage classification.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data.Journal of biological rhythms · 2025Article
- A Lightweight Neural Network Based on Memory and Transition Probability for Accurate Real-Time Sleep Stage Classification.Brain sciences · 2025Article
- CB-1 receptor agonist drastically changes oscillatory activity, defining active sleep.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Contribution of basal ganglia activity to REM sleep disorder in Parkinson's disease.Journal of neurology, neurosurgery, and psychiatry · 2024Article
- Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine.CNS neuroscience & therapeutics · 2024Article
- An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight.Frontiers in neuroinformatics · 2024Article
- Sleep-Deep-Learner is taught sleep-wake scoring by the end-user to complete each record in their style.Sleep advances : a journal of the Sleep Research Society · 2024Article
- Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Sleep scoring plays a pivotal role both in sleep research and in clinical practice. Traditionally, this process has relied on manual scoring by human experts, but it is marred by time constraints, and inconsistencies between different scorers. Consequently, the quest for more efficient and reliable approaches has sparked a great interest in the realm of automatic sleep-scoring methods. In this article, we provide an exploration of the merits and drawbacks of automatic sleep scoring, alongside the pressing challenges and critical considerations that demand attention in this evolving field.
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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.