ReviewSovremennye tekhnologii v meditsine2022
Trends in Population-Based Studies: Molecular and Digital Epidemiology (Review).
Review in Sovremennye tekhnologii v meditsine, 2022. 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.
- Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024).JMIR public health and surveillance · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
The development of high-throughput technologies has sharply increased the opportunities to research the human body at the molecular, cellular, and organismal levels in the last decade. Rapid progress in biotechnology has caused a paradigm shift in population-based studies. Advances in modern biomedical sciences, including genomic, genome-wide, post-genomic research and bioinformatics, have contributed to the emergence of molecular epidemiology focused on the study of the personalized molecular mechanism of disease development and its extrapolation to the population level. The work of research teams at the intersection of information technology and medicine has become the basis for highlighting digital epidemiology, the important tools of which are machine learning, the ability to work with real world data, and accumulated big data. The developed approaches accelerate the process of collecting and processing biomedical data, testing new scientific hypotheses. However, new methods are still in their infancy, they require testing of application under various conditions, as well as standardization. This review highlights the role of omics and digital technologies in population-based studies.
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.