ReviewFrontiers in oncology2015
Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery.
Review in Frontiers in oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- A Systematic Review of Techniques and Sources of Big Data in the Healthcare Sector.Journal of medical systems · 2017Pooled it
- From Semantic Modeling to Precision Radiotherapy: An AI Framework Linking Radiobiology, Oncology, and Public Health Integration.Biomedicines · 2025Review
- Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.JCO clinical cancer informatics · 2023Article
- Translating Data Science Results into Precision Oncology Decisions: A Mini Review.Journal of clinical medicine · 2023Review
- Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer.EGEMS (Washington, DC) · 2018Article
- Selection of external beam radiotherapy approaches for precise and accurate cancer treatment.Journal of radiation research · 2018Article
- How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology.Frontiers in oncology · 2018Article
- Big data in oncologic imaging.La Radiologia medica · 2017Article
- Big Data in Health: a Literature Review from the Year 2005.Journal of medical systems · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several advances in large data set collection and processing have the potential to provide a wave of new insights and improvements in the use of radiation therapy for cancer treatment. The era of electronic health records, genomics, and improving information technology resources creates the opportunity to leverage these developments to create a learning healthcare system that can rapidly deliver informative clinical evidence. By merging concepts from comparative effectiveness research with the tools and analytic approaches of "big data," it is hoped that this union will accelerate discovery, improve evidence for decision making, and increase the availability of highly relevant, personalized information. This combination offers the potential to provide data and analysis that can be leveraged for ultra-personalized medicine and high-quality, cutting-edge radiation therapy.
Indexed as
Identifiers
What OpenQuestion holds
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.