Evidence map›Paper›PMID 39630700›Full record

ArticlePloS one2024

Exploring the potential of large language models for integration into an academic statistical consulting service-the EXPOLS study protocol.

Urs Alexander Fichtner, Jochen Knaus, Erika Graf, Georg Koch, Jörg Sahlmann, Dominikus Stelzer, Martin Wolkewitz, Harald Binder, Susanne Weber

Abstract read
In one paragraph

Article in PloS one, 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

9 authors.

Urs Alexander FichtnerFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, Section for Healthcare Research and Rehabilitation Research (SEVERA), University of Freiburg, Freiburg, Germany.ORCID 0000-0002-4190-8969
Jochen KnausFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.
Erika GrafFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.ORCID 0000-0002-6831-0756
Georg KochFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.
Jörg SahlmannFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.ORCID 0000-0003-3023-3318
Dominikus StelzerFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.ORCID 0000-0001-7970-6388
Martin WolkewitzFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.
Harald BinderFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.ORCID 0000-0002-5666-8662
Susanne WeberFaculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany.ORCID 0000-0002-4418-3836

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe advancement of Artificial Intelligence, particularly Large Language Models (LLMs), is rapidly progressing. LLMs, such as OpenAI's GPT, are becoming vital in scientific and medical processes, including text production, knowledge synthesis, translation, patient communication and data analysis. However, the outcome quality needs to be evaluated to assess the full potential for usage in statistical applications. LLMs show potential for all research areas, including teaching. Integrating LLMs in research, education and medical care poses opportunities and challenges, depending on user competence, experience and attitudes.

objectiveThis project aims at exploring the use of LLMs in supporting statistical consulting by evaluating the utility, efficiency and satisfaction related to the use of LLMs in statistical consulting from both advisee and consultant perspective. Within this project, we will develop, execute and evaluate a training module for the use of LLMs in statistical consulting. In this context, we aim to identify the strengths, limitations and areas for potential improvement. Furthermore, we will explore experiences, attitudes, fears and current practices regarding the use of LLMs of the staff at the Medical Center and the University of Freiburg. MATERIALS AND

methodsThis multimodal study includes four study parts using qualitative and quantitative methods to gather data. Study part (I) is designed as mixed mode study to explore the use of LLMs in supporting statistical consulting and to evaluate the utility, efficiency and satisfaction related to the use of LLMs. Study part (II) uses a standardized online questionnaire to evaluate the training module. Study part (III) evaluates the consulting sessions using LLMs from advisee perspective. Study part (IV) explores experiences, attitudes, fears and current practices regarding the use of LLMs of the staff at the Medical Center and the University of Freiburg. This study is registered at the Freiburg Registry of Clinical Studies under the ID: FRKS004971.

Indexed as

Artificial IntelligenceHumansLanguageSurveys and Questionnaires

Identifiers

PMID39630700
PMCPMC11616834

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.