Evidence map›Paper›PMID 39185523›Full record

ArticlemedRxiv : the preprint server for health sciences2024

The quality of data-driven hypotheses generated by inexperienced clinical researchers: A case study.

Mytchell A Ernst, Brooke N Draghi, James J Cimino, Vimla L Patel, Yuchun Zhou, Jay H Shubrook, Sonsoles De Lacalle, Aneesa Weaver, Chang Liu, Xia Jing

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Mytchell A ErnstDepartment of Public Health Sciences, Clemson University, Clemson, SC.ORCID 0009-0009-7762-3349
Brooke N DraghiDepartment of Public Health Sciences, Clemson University, Clemson, SC.ORCID 0000-0002-2108-7382
James J CiminoDepartment of Biomedical Informatics and Data Science, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL.ORCID 0000-0003-4101-1622
Vimla L PatelCognitive Studies in Medicine and Public Health, The New York Academy of Medicine, New York City, NY.ORCID 0000-0003-1656-6642
Yuchun ZhouPatton College of Education, Ohio University, Athens, OH.ORCID 0000-0002-0925-5507
Jay H ShubrookDepartment of Clinical Sciences and Community Health, Touro University California College of Osteopathic Medicine, Vallejo, CA.ORCID 0000-0001-5173-2656
Sonsoles De LacalleDepartment of Health Science, California State University Channel Islands, Camarillo, CA.ORCID 0000-0001-9594-698X
Aneesa WeaverDepartment of Public Health Sciences, Clemson University, Clemson, SC.
Chang LiuRuss College of Engineering and Technology, Ohio University, Athens, OH.ORCID 0000-0002-6721-1959
Xia JingDepartment of Public Health Sciences, Clemson University, Clemson, SC.ORCID 0000-0002-1916-4588

Funding

Tissue Structural and Neural Remodeling in Human Sacroiliac JointP20GM121342 · NIGMS · CLEMSON UNIVERSITY · PI Hai Yao · 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

Objectives: We invited inexperienced clinical researchers to analyze coded health datasets and develop hypotheses. We recorded and analyzed their hypothesis generation process. All the hypotheses generated in the process were rated by the same group of seven experts by using the same metrics. This case study examines the higher quality (i.e., higher ratings) and lower quality of hypotheses and participants who generated them. We characterized the contextual factors associated with the quality of hypotheses. Methods: All participants (i.e., clinical researchers) completed a 2-hour study session to analyze data and generate scientific hypotheses using the think-aloud method. Participants' screen activity and audio were recorded and transcribed. These transcriptions were used to measure the time used to generate each hypothesis and to code cognitive events (i.e., cognitive activities used when generating hypotheses, for example, "Seeking for Connection" describes an attempt to draw connections between data points). The hypothesis ratings by the expert panel were used as the quality of the hypotheses during the analysis. We analyzed the factors associated with (1) the five highest and (2) five lowest rated hypotheses and (3) the participants who generated them, including the number of hypotheses per participant, the validity of those hypotheses, the number of cognitive events used for each hypothesis, as well as the participant's research experience and basic demographics. Results: Participants who generated the five highest-rated hypotheses used similar lengths of time (difference 3:03), whereas those who generated the five lowest-rated hypotheses used more varying lengths of time (difference 7:13). Participants who generated the five highest-rated hypotheses also utilized slightly fewer cognitive events on average compared to the five lowest-rated hypotheses (4 per hypothesis vs. 4.8 per hypothesis). When we examine the participants (who generated the five highest and five lowest hypotheses) and their total hypotheses generated during the 2-hour study sessions, the participants with the five highest-rated hypotheses again had a shorter range of time per hypothesis on average (0:03:34 vs. 0:07:17). They (with the five highest ratings) used fewer cognitive events per hypothesis (3.498 vs. 4.626). They (with the five highest ratings) also had a higher percentage of valid rate (75.51% vs. 63.63%) and generally had more experience with clinical research. Conclusion: The quality of the hypotheses was shown to be associated with the time taken to generate them, where too long or too short time to generate hypotheses appears to be negatively associated with the hypotheses' quality ratings. Also, having more experience seems to positively correlate with higher ratings of hypotheses and higher valid rates. Validity is a quality dimension used by the expert panel during rating. However, we acknowledge that our results are anecdotal. The effect may not be simply linear, and future research is necessary. These results underscore the multi-factor nature of hypothesis generation.

Indexed as

case studyclinical researchresearch designScientific hypothesis generation

Identifiers

PMID39185523
PMCPMC11343241

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.