ArticleJournal of medical Internet research2014
Collaborative biomedicine in the age of big data: the case of cancer.
Article in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A patient-level data meta-analysis of standard-of-care treatments from eight prostate cancer clinical trials.Scientific data · 2016Pooled it
- Will Big Data and personalized medicine do the gender dimension justice?AI & society · 2023Article
- Cardiovascular informatics: building a bridge to data harmony.Cardiovascular research · 2022Review
- Emerging digital technologies in cancer treatment, prevention, and control.Translational behavioral medicine · 2021Review
- Team Science in Precision Medicine: Study of Coleadership and Coauthorship Across Health Organizations.Journal of medical Internet research · 2021Article
- Personalized, Predictive, Participatory, Precision, and Preventive (P5) Medicine in Rotator Cuff Tears.Journal of personalized medicine · 2021Article
- A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study.Journal of medical Internet research · 2020Article
- An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review.Journal of medical Internet research · 2020Article
- How to preserve the original mission of research in the omics era?EMBO reports · 2020Article
- Increasing the Impact of JMIR Journals in the Attention Economy.Journal of medical Internet research · 2019Article
- Role of the Internet in Solving the Last Mile Problem in Medicine.Journal of medical Internet research · 2019Article
- Biomarkers: Delivering on the expectation of molecularly driven, quantitative health.Experimental biology and medicine (Maywood, N.J.) · 2018Review
- Machine learning to support social media empowered patients in cancer care and cancer treatment decisions.PloS one · 2018Article
- Big Data's Role in Precision Public Health.Frontiers in public health · 2018Review
- Future cancer research priorities in the USA: a Lancet Oncology Commission.The Lancet. Oncology · 2017Review
- The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.Implementation science : IS · 2017Article
- Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical data.Journal of biomedical informatics · 2016Article
- Towards the Design of a Patient-Specific Virtual Tumour.Computational and mathematical methods in medicine · 2016Article
- DO CANCER CLINICAL TRIAL POPULATIONS TRULY REPRESENT CANCER PATIENTS? A COMPARISON OF OPEN CLINICAL TRIALS TO THE CANCER GENOME ATLAS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Pharmacy Practice, Education, and Research in the Era of Big Data: 2014-15 Argus Commission Report.American journal of pharmaceutical education · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomedicine is undergoing a revolution driven by high throughput and connective computing that is transforming medical research and practice. Using oncology as an example, the speed and capacity of genomic sequencing technologies is advancing the utility of individual genetic profiles for anticipating risk and targeting therapeutics. The goal is to enable an era of "P4" medicine that will become increasingly more predictive, personalized, preemptive, and participative over time. This vision hinges on leveraging potentially innovative and disruptive technologies in medicine to accelerate discovery and to reorient clinical practice for patient-centered care. Based on a panel discussion at the Medicine 2.0 conference in Boston with representatives from the National Cancer Institute, Moffitt Cancer Center, and Stanford University School of Medicine, this paper explores how emerging sociotechnical frameworks, informatics platforms, and health-related policy can be used to encourage data liquidity and innovation. This builds on the Institute of Medicine's vision for a "rapid learning health care system" to enable an open source, population-based approach to cancer prevention and control.
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.