Evidence map›Paper›PMID 39211055›Full record

ArticleMedical research archives2024

Data-Driven Hypothesis Generation in Clinical Research: What We Learned from a Human Subject Study?

Xia Jing, James J Cimino, Vimla L Patel, Yuchun Zhou, Jay H Shubrook, Chang Liu, Sonsoles De Lacalle

Abstract read
In one paragraph

Article in Medical research archives, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xia JingDepartment of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, SC.
James J CiminoInformatics Institute, School of Medicine, University of Alabama, Birmingham, Birmingham, AL.
Vimla L PatelCognitive Studies in Medicine and Public Health, The New York Academy of Medicine, New York City, NY.
Yuchun ZhouDepartment of Educational Studies, Patton College of Education, Ohio University, Athens, OH.
Jay H ShubrookDepartment of Clinical Sciences and Community Health, Touro University California College of Osteopathic Medicine, Vallejo, CA.
Chang LiuDepartment of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, Athens, OH.
Sonsoles De LacalleDepartment of Health Science, California State University Channel Islands, Camarillo, CA.

Funding

Tissue Structural and Neural Remodeling in Human Sacroiliac JointP20GM121342 · NIGMS · CLEMSON UNIVERSITY · PI Peng Chen · 2018 to 2026
$24.7M
SC Biomedical Informatics & Data Science for Health Impact (SC BIDS4Health)T15LM013977 · NLM · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI Alexander V Alekseyenko, Brian Dean · 2022 to 2026
$1.2M
Exploration of the hypothesis-generation process as guided by secondary data analysis in clinical research: Can the process be more efficient?R15LM012941 · NLM · OHIO UNIVERSITY ATHENS · PI JING, XIA · 2018 to 2018
$394k
NIGMS NIH HHS P20 GM121342NLM NIH HHS R15 LM012941NLM NIH HHS T15 LM013977
6 · The paper itself

Abstract

Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS-a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

Indexed as

Clinical researchdata-driven hypothesis generationmedical informaticsscientific hypothesis generationtranslational researchvisualization

Identifiers

PMID39211055
PMCPMC11361316

What OpenQuestion holds

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Registered trials

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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.