ArticleInteractive journal of medical research2023
Normal Workflow and Key Strategies for Data Cleaning Toward Real-World Data: Viewpoint.
Article in Interactive journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- A reproducible entropy-weighted Gower-CLARA-TOPSIS procedure for mixed-type MSME segmentation and prioritization.MethodsX · 2026Article
- A decade of Cybathlon: impact on public visibility, scientific dissemination and technology transfer.Journal of neuroengineering and rehabilitation · 2026Article
- Use of Incurred Sample Reproducibility Data to Support Long Term Stability of Therapeutic Antibodies in Clinical Bioanalytical Methods.AAPS PharmSciTech · 2026Article
- Real-World Evidence: Methodologies for Integrating Real-World Data into Clinical Research Frameworks.Journal of orthopaedic case reports · 2025Article
- Characteristics associated with women undergoing their first mammography screening at a younger age.PLOS global public health · 2025Article
- Advancing the safe motherhood initiative: A qualitative and sentiment analysis of local physician's perspectives on antibiotic self-medication during pregnancy in a low- and middle-income country.PLOS global public health · 2025Article
- BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of science, technology, and engineering, large amounts of data have been generated in many fields in the past 20 years. In the process of medical research, data are constantly generated, and large amounts of real-world data form a "data disaster." Effective data analysis and mining are based on data availability and high data quality. The premise of high data quality is the need to clean the data. Data cleaning is the process of detecting and correcting "dirty data," which is the basis of data analysis and management. Moreover, data cleaning is a common technology for improving data quality. However, the current literature on real-world research provides little guidance on how to efficiently and ethically set up and perform data cleaning. To address this issue, we proposed a data cleaning framework for real-world research, focusing on the 3 most common types of dirty data (duplicate, missing, and outlier data), and a normal workflow for data cleaning to serve as a reference for the application of such technologies in future studies. We also provided relevant suggestions for common problems in data cleaning.
Indexed as
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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.