Evidence map›Paper›PMID 41413427›Full record

ArticleScientific data2025

An Egocentric Life-Saving Interventional Procedure Dataset of Actions, Medical Questions, Maneuvers and Tools.

Yupeng Zhuo, Eddie Zhang, Xiangchen Yu, Aditya Pachpande, Wenqu Fang, Xiyue Chen, Andrew W Kirkpatrick, Kyle Couperus, Christopher Colombo, Oanh Tran and 7 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. 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

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

17 authors.

Yupeng ZhuoPurdue University, Edwardson School of Industrial Engineering, West Lafayette, IN, 47906, USA.ORCID http://orcid.org/0000-0002-8538-6074
Eddie ZhangPurdue University, Edwardson School of Industrial Engineering, West Lafayette, IN, 47906, USA.
Xiangchen YuPurdue University, Edwardson School of Industrial Engineering, West Lafayette, IN, 47906, USA.
Aditya PachpandePurdue University, Edwardson School of Industrial Engineering, West Lafayette, IN, 47906, USA.
Wenqu FangPurdue University, Edwardson School of Industrial Engineering, West Lafayette, IN, 47906, USA.
Xiyue ChenPurdue University, Edwardson School of Industrial Engineering, West Lafayette, IN, 47906, USA.
Andrew W KirkpatrickUniversity of Calgary, Calgary, Alberta, T2N 1N4, Canada.
Kyle CouperusMadigan Army Medical Center, Tacoma, WA, 98431, USA.
Christopher ColomboMadigan Army Medical Center, Tacoma, WA, 98431, USA.
Oanh TranMadigan Army Medical Center, Tacoma, WA, 98431, USA.
Jonah BeckMadigan Army Medical Center, Tacoma, WA, 98431, USA.
DeAnna DeVaneMadigan Army Medical Center, Tacoma, WA, 98431, USA.
Jessica McKeeUniversity of Calgary, Calgary, Alberta, T2N 1N4, Canada.
Chad GorbatkinMadigan Army Medical Center, Tacoma, WA, 98431, USA.
Ross CandeloreMadigan Army Medical Center, Tacoma, WA, 98431, USA.
Eleanor BirchMadigan Army Medical Center, Tacoma, WA, 98431, USA.
Juan WachsPurdue University, Edwardson School of Industrial Engineering, West Lafayette, IN, 47906, USA. jpwachs@purdue.edu.

Funding

National Science Foundation (NSF) 2140612United States Department of Defense | United States Army | Army Medical Command | U.S. Army Research Institute of Environmental Medicine (United States Army Research Institute of Environmental Medicine) W81XWH21C0119
6 · The paper itself

Abstract

This paper introduces the Trauma THOMPSON dataset, designed to advance AI-driven decision support for life-saving interventions (LSIs) in emergency care, particularly in resource constrained humanitarian settings. The dataset comprises 3,717 high resolution and egocentric video clips of both regular and just-in-time (JIT) procedures. The JIT procedures consists of videos of the same LSI procedures, but with makeshift tools, and is useful for studying human medical commonsense. Each clip is annotated by medical professionals with verb-noun format, such as "take scalpel" and "make incision". In addition to action segments, the dataset includes annotations for medical visual question answering (MVQA), hand maneuvers, and object detection. Eventually, these rich annotations and dataset can be used to train an AI agent to advise first-responders in the field about what to do next with the resources at hand. We provide benchmarks for action recognition, anticipation, and MVQA using state-of-the-art machine learning models.

Indexed as

Artificial IntelligenceEmergency Medical ServicesHumansMachine LearningVideo Recording

Identifiers

PMID41413427
PMCPMC12815934

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.