ArticleAnnals of thoracic medicine
Comparing polysomnography auto scoring with the standard of care in sleep medicine.
Article in Annals of thoracic medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A Lightweight Temporal-Spatial Fusion Network for Neonatal Sleep Staging.Bioengineering (Basel, Switzerland) · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundManual polysomnography is considered as the gold-standard for diagnosis of sleep disorders. However, it is a labor intensive and time-consuming task and is being replaced by automated sleep scoring systems.
methodsThis cross-sectional study was conducted at Sleep Medicine and Research Center, King Abdulaziz University Hospital, Jeddah, Saudi Arabia, between January 2022 and July 2024, the correlation and agreement between various parameters recorded through automated and manual sleep scoring.
resultsOf a total of 442 participants, 234 (53.4%) were male and 206 (46.6%) were female. The mean age of participants was 48 ± 18.5 years. A moderate degree of agreement was found between apnea-hypopnea index (AHI) (κ = 0.510) and respiratory disturbance index supine (κ = 0.474) recorded through automated and manual scoring (
conclusionThere appears to be a marked variation in the accuracy of various sleep parameters by automated sleep recording. Further studies that utilize advance machine learning algorithms can help better understanding of the role of automated sleep scoring in screening and diagnosis of sleep disorders.
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.