Evidence map›Paper›PMID 37958007›Full record

ArticleHealthcare (Basel, Switzerland)2023

Development of Machine Learning Model for VO

Hyun Ah Lee, Woosik Yu, Jong Doo Choi, Young-Sin Lee, Ji Won Park, Yun Jung Jung, Seung Soo Sheen, Junho Jung, Seokjin Haam, Sang Hun Kim and 1 more

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06896734 (Evaluation of the mobiCARE™ ECG Monitoring System Electrode Placement Positioning Limits in a Prospective, Non-Randomized, Single-Center U.S. Study), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 23% of its field
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.

NCT06896734 completednot on this mapstarted 2025, after this paper: background citation

Evaluation of the mobiCARE™ ECG Monitoring System Electrode Placement Positioning Limits in a Prospective, Non-Randomized, Single-Center U.S. Study

TypeobservationalSponsorSeers Technology Co., Ltd.Ran2025 to 2025Enrolled49ConditionsECG MonitoringArmsEvaluation of the ECG signal in the nominal location and in three alternate positions
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
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 at 2 institutions in 1 country.

Hyun Ah LeeDepartment of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea.ORCID 0000-0001-9219-804X
Woosik YuDepartment of Thoracic and Cardiovascular Surgery, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Jong Doo ChoiSeers Technology Co., Seongnam-si 13558, Republic of Korea.ORCID 0000-0002-0286-8124
Young-Sin LeeSeers Technology Co., Seongnam-si 13558, Republic of Korea.
Ji Won ParkDepartment of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Yun Jung JungDepartment of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea.ORCID 0000-0002-8887-0881
Seung Soo SheenDepartment of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Junho JungDepartment of Thoracic and Cardiovascular Surgery, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Seokjin HaamDepartment of Thoracic and Cardiovascular Surgery, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Sang Hun KimDepartment of Rehabilitation Medicine, Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Republic of Korea.ORCID 0000-0003-4849-5228
Ji Eun ParkDepartment of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea.ORCID 0000-0002-3035-6353
Ajou University · KRPusan National University Hospital · KR

Funding

Ministry of Health & Welfare, Republic of Korea HI20C2140
6 · The paper itself

Abstract

A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO

Indexed as

cardiopulmonary exercise test (CPET)estimationlung resection candidatesmachine learning modelmaximal oxygen consumption (VO2max)

Identifiers

PMID37958007
PMCPMC10648477
OpenAlexW4388108264

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.