ArticleJournal of healthcare engineering2021
Application Analysis Based on Big Data Technology in Stroke Rehabilitation Nursing.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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.
- Design and implementation of informatization for unified management of stroke rehabilitation in urban multi-level hospitals.Frontiers in neuroscience · 2023Article
- Retracted: Application Analysis Based on Big Data Technology in Stroke Rehabilitation Nursing.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-12-06Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the statistical analysis, the incidence of stroke disease has gradually increased, particularly in recent years, which poses a huge threat to the safety of human life. Due to the advancement in science and technology specifically big data and sensors, a new research dome known as data mining technology has been introduced, which has the potential value from the perspective of large amount of data analysis. Information has become a new trend of science and technology, and data mining has been used in various application areas to analyze and predict strokes at home and abroad. In this study, big data technology is utilized to collect potential information and explores clinical pathways of level-3 rehabilitation in certain regions of China. Moreover, application effects of data mining in the rehabilitation of patients with the first ischemic stroke have been evaluated and reported. For this purpose, fifty (50) first-time ischemic stroke patients have been screened through big data and were nonartificially assigned to level-3 clinical pathway and conventional rehabilitation groups, respectively, specifically through software. The first group of patients enters the clinical path of the corresponding level according to the way of three-level referral. These patients were analyzed based on the collected results of completing the unified rehabilitation treatment plan of the three-level rehabilitation medical institution in the patient record form. The second group was selected according to the routine rehabilitation model and method of the medical institution where the patients visited were divided into four stages: before treatment, three weeks after treatment, nine weeks after treatment, and seventeen weeks after treatment. For this purpose, a simplified Fugl-Meyer analysis (FMA), recording of various functions of limb movement, and modified Barthel index (MBI) scale were used to analyze and evaluate the ability of daily activities and compare their effects. The final results showed that FMA and MBI scores of the two groups were improved in the three stages after treatment. The FMA and MBI scores of the clinical pathway group on 3rd and 9th weekends were significantly different from those of the conventional rehabilitation group (which is
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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.