Evidence map›Paper›PMID 33733183›Full record

ArticleFrontiers in artificial intelligence2020

Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Matt Mistro, Yang Sheng, Yaorong Ge, Chris R Kelsey, Jatinder R Palta, Jing Cai, Qiuwen Wu, Fang-Fang Yin, Q Jackie Wu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2020. 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
0.3field-weighted citation impact, top 36% 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.

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, 6 citations in OpenAlex.

  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 at 5 institutions in 2 countries.

Matt MistroDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
Yang ShengDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
Yaorong GeDepartment of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, United States.
Chris R KelseyDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
Jatinder R PaltaDepartment of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, United States.
Jing CaiDepartment of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China.
Qiuwen WuDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
Fang-Fang YinDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
Q Jackie WuDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
Duke Medical Center · USDuke University · USHong Kong Polytechnic University · HKUniversity of North Carolina at Charlotte · USVirginia Commonwealth University · US

Funding

Developing knowledge models to enable rapid learning in radiation therapyR01CA201212 · NCI · DUKE UNIVERSITY · PI GE, YAORONG, WU, QINGRONG JACKIE · 2016 to 2019
$1.8M
NCI NIH HHS R01 CA201212
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

intensity modulated radiation therapyknowledge modellung cancermachine learningtutoring system

Identifiers

PMID33733183
PMCPMC7861316
OpenAlexW3081637874

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.