ReviewCureus2024
Visualizing Scholarly Trends in Stochastic Models for Disease Prediction.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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.
- FLASH radiotherapy at a crossroads: a bibliometric perspective on progress and challenges.Discover oncology · 2025Article
- Global research landscape, hotspots, and emerging trends of digital biomarkers: A bibliometric analysis.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stochastic models play a pivotal role in disease prediction by accounting for randomness and uncertainty in biological systems. This study offers a visualization of trends in the application of stochastic models for disease prediction from 1990 to 2024, based on a bibliometric analysis of Scopus data. Key findings reveal a significant growth in research post-2014, largely driven by global health challenges like COVID-19. Despite these advancements, gaps remain in applying these models to non-communicable diseases and low-resource settings. By integrating computational techniques like machine learning, stochastic models hold promise for improving predictive accuracy. This study highlights the need for further international collaboration and interdisciplinary research, offering practical insights for researchers and public health professionals aiming to enhance disease prediction and intervention strategies.
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.