Evidence map›Paper›PMID 30226286›Full record

ArticleMedical physics2018

Treatment data and technical process challenges for practical big data efforts in radiation oncology.

C S Mayo, M Phillips, T R McNutt, J Palta, A Dekker, R C Miller, Y Xiao, J M Moran, M M Matuszak, P Gabriel and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. 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

22 authors.

C S MayoUniversity of Michigan, Ann Arbor, MI, USA.
M PhillipsUniversity of Washington, Seattle, WA, USA.
T R McNuttJohns Hopkins University, Baltimore, MD, USA.
J PaltaVirginia Commonwealth University, Richmond, VA, USA.
A DekkerDepartment of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
R C MillerMayo Clinic, Jacksonville, FL, USA.
Y XiaoUniversity of Pennsylvania, Philadelphia, PA, USA.
J M MoranUniversity of Michigan, Ann Arbor, MI, USA.
M M MatuszakUniversity of Michigan, Ann Arbor, MI, USA.
P GabrielUniversity of Pennsylvania, Philadelphia, PA, USA.
A S AyanOhio State University, Columbus, OH, USA.
J PrisciandaroUniversity of Michigan, Ann Arbor, MI, USA.
M ThorMemorial Sloan Kettering Cancer Center, New York, NY, USA.
N DixitUniversity of California at San Francisco, San Francisco, CA, USA.
R PoppleUniversity of Alabama at Birmingham, Birmingham, AL, USA.
J KilloranHarvard Medical School, Boston, MA, USA.
E KalebaUniversity of Michigan, Ann Arbor, MI, USA.
M KantorMD Anderson Cancer Center, Houston, TX, USA.
D RuanUniversity of California at Los Angeles, Los Angeles, CA, USA.
R KapoorJohns Hopkins University, Baltimore, MD, USA.
M L KesslerUniversity of Michigan, Ann Arbor, MI, USA.
T S LawrenceUniversity of Michigan, Ann Arbor, MI, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
AAPMElektaNCI NIH HHS P30 CA008748Varian Medical Systems
6 · The paper itself

Abstract

The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.

Indexed as

Databases, FactualData MiningHumansInformation Storage and RetrievalMedical InformaticsMotivationNeoplasmsNeoplasm StagingRadiation Oncologybig datainformaticsmachine learningontologystandardization

Identifiers

PMID30226286
PMCPMC8082598

What OpenQuestion holds

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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.