ArticleSensors (Basel, Switzerland)2019
Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Occupational exposure to video display terminals: a systematic review of ocular and visual health effects and associated risk factors.Journal of occupational health · 2026Pooled it
- Prevalence of computer vision syndrome: A systematic review and meta-analysis.Journal of optometryPooled it
- Objective Assessment of Visual Workload in Video Display Terminal Workers Using a Non-Invasive Monitoring System.La Medicina del lavoro · 2026Observational
- Computer vision syndrome among medical students at Kasr Al-Ainy Faculty of Medicine, Cairo University, Egypt: a cross-sectional study of prevalence, risk factors, and protective measures.Frontiers in public health · 2026Article
- PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia.European psychiatry : the journal of the Association of European Psychiatrists · 2024Article
- Objective Evaluation of Active Interactions between the Operator and Display Screen Equipment Using an Innovative Acquisition System.Bioengineering (Basel, Switzerland) · 2023Article
- Prevalence of Digital Eye Strain Among University Students and Its Association with Virtual Learning During the COVID-19 Pandemic.Clinical ophthalmology (Auckland, N.Z.) · 2023Article
- AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network.Scientific reports · 2022Article
- Use of ambient lighting during colonoscopy and its effect on adenoma detection rate and eye fatigue: results of a pilot study.Endoscopy international open · 2021Article
- Digital eye strain: prevalence and associated factors among information technology professionals, Egypt.Environmental science and pollution research international · 2021Article
- Digital Eye Strain and Its Risk Factors Among a University Student Population in Jordan: A Cross-Sectional Study.Cureus · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage.
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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.