Evidence map›Paper›PMID 32229032›Full record

ReviewSeminars in oncology2020

Using big data in pediatric oncology: Current applications and future directions.

Ajay Major, Suzanne M Cox, Samuel L Volchenboum

Abstract readReview
In one paragraph

Review in Seminars in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

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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

3 authors.

Ajay MajorSection of Hematology Oncology, University of Chicago Medicine, Chicago, IL.
Suzanne M CoxBiological Sciences Division, University of Chicago, Chicago, IL.
Samuel L VolchenboumPediatric Hematology Oncology, University of Chicago, Chicago, IL. Electronic address: slv@uchicago.edu.

Funding

BASIC RESEARCH TRAINING IN MEDICAL ONCOLOGYT32CA009566 · NCI · UNIVERSITY OF CHICAGO · PI OLUFUNMILAYO F. OLOPADE · 1987 to 2026
$10.5M
NCI NIH HHS T32 CA009566
6 · The paper itself

Abstract

Pediatric cancer is a rare disease with a low annual incidence, which presents a significant challenge in being able to collect enough data to fuel clinical discoveries. Big data registry trials hold promise to advance the study of pediatric cancers by allowing for the combination of traditional randomized controlled trials with the power of larger cohort sizes. The emergence of big data resources and data-sharing initiatives are becoming transformative for pediatric cancer diagnosis and treatment. This review discusses the uses of big data in pediatric cancer, existing pediatric cancer registry initiatives and research, the challenges in harmonizing these data to improve accessibility for study, and building pediatric data commons and other important future endeavors.

Indexed as

Big DataAge FactorsChildDatabases, FactualHumansInformation DisseminationMedical InformaticsMedical OncologyNeoplasmsPediatricsPublic Health SurveillanceRegistriesResearchSurvivorshipBig dataData scienceData sharingInformaticsPediatric cancerPediatric oncology

Identifiers

PMID32229032
PMCPMC9036548

What OpenQuestion holds

Textmetadata
LicenceTDM
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.