Evidence map›Paper›PMID 38455840›Full record

ArticleJAMIA open2024

Retrieval augmented scientific claim verification.

Hao Liu, Ali Soroush, Jordan G Nestor, Elizabeth Park, Betina Idnay, Yilu Fang, Jane Pan, Stan Liao, Marguerite Bernard, Yifan Peng and 1 more

Abstract read
In one paragraph

Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Hao LiuSchool of Computing, Montclair State University, Montclair, NJ 07043, United States.ORCID https://orcid.org/0000-0002-1975-1272
Ali SoroushDepartment of Medicine, Columbia University, New York, NY 10027, United States.
Jordan G NestorDepartment of Medicine, Columbia University, New York, NY 10027, United States.ORCID https://orcid.org/0000-0003-1418-3103
Elizabeth ParkDepartment of Medicine, Columbia University, New York, NY 10027, United States.
Betina IdnayDepartment of Biomedical Informatics, Columbia University, New York, NY 10027, United States.ORCID https://orcid.org/0000-0002-4318-5987
Yilu FangDepartment of Biomedical Informatics, Columbia University, New York, NY 10027, United States.ORCID https://orcid.org/0000-0002-2681-1931
Jane PanDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, United States.
Stan LiaoDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, United States.
Marguerite BernardInstitute of Human Nutrition, Columbia University, New York, NY 10027, United States.
Yifan PengDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States.ORCID https://orcid.org/0000-0001-9309-8331
Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, NY 10027, United States.

Funding

Clinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0M
Research Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Miguel Arce · 2020 to 2026
$30.1M
Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical DataR01LM009886 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WENG, CHUNHUA · 2009 to 2020
$5.3M
ClinEX - Clinical Evidence Extraction, Representation, and AppraisalR01LM014344 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Yong Chen, Yifan Peng · 2023 to 2026
$2.7M
Deep learning based predictive analytics to improve patient health.R01LM013061 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Krzysztof Kiryluk, CHUNHUA WENG · 2019 to 2026
$2.7M
NCATS NIH HHS UL1 TR001873NIA NIH HHS P30 AG066462NLM NIH HHS R01 LM009886NLM NIH HHS R01 LM013061NLM NIH HHS R01 LM014344NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

Objective: To automate scientific claim verification using PubMed abstracts. Materials and Methods: We developed CliVER, an end-to-end scientific Results: In the evaluation of label prediction accuracy on CoVERt, CliVER achieved a notable F1 score of 0.92, highlighting the efficacy of the retrieval-augmented models. The ensemble model outperforms each individual state-of-the-art model by an absolute increase from 3% to 11% in the F1 score. Moreover, when compared with four clinicians, CliVER achieved a precision of 79.0% for abstract retrieval, 67.4% for sentence selection, and 63.2% for label prediction, respectively. Conclusion: CliVER demonstrates its early potential to automate scientific claim verification using retrieval-augmented strategies to harness the wealth of clinical trial abstracts in PubMed. Future studies are warranted to further test its clinical utility.

Indexed as

clinical trialdeep learningevidence appraisalevidence retrievalnatural language processing

Identifiers

PMID38455840
PMCPMC10919922

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.