ArticleEGEMS (Washington, DC)2018
Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer.
Article in EGEMS (Washington, DC), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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
29 citing papers in PubMed.
- Identifying organization-level features important to patient outcomes in cancer care: qualitative interview results with experts.BMC health services research · 2026Article
- Challenges in analyzing real-world evidence: analysis of androgen deprivation therapy use for prostate cancer in the REASSURE study.ESMO real world data and digital oncology · 2025Review
- Article
- Creating a data warehouse to support monitoring of NSQHS blood management standard from EMR data.BMC medical informatics and decision making · 2024Article
- Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.JCO clinical cancer informatics · 2023Article
- Enhancing an enterprise data warehouse for research with data extracted using natural language processing.Journal of clinical and translational science · 2023Article
- Outcomes of Primary Trabeculectomy versus Combined Phacoemulsification-Trabeculectomy Using Automated Electronic Health Record Data Extraction.Current eye research · 2022Article
- Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage.Frontiers in digital health · 2022Article
- Evaluation of clustering and topic modeling methods over health-related tweets and emails.Artificial intelligence in medicine · 2021Article
- Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience.JCO clinical cancer informatics · 2021Article
- Assessment of a Clinical Trial-Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer.JAMA network open · 2021Article
- Real-world Evidence to Estimate Prostate Cancer Costs for First-line Treatment or Active Surveillance.European urology open science · 2021Article
- Phenotyping severity of patient-centered outcomes using clinical notes: A prostate cancer use case.Learning health systems · 2020Article
- Content Coverage Evaluation of the OMOP Vocabulary on the Transplant Domain Focusing on Concepts Relevant for Kidney Transplant Outcomes Analysis.Applied clinical informatics · 2020Article
- Leveraging Digital Data to Inform and Improve Quality Cancer Care.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- Insights from Adopting a Data Commons Approach for Large-scale Observational Cohort Studies: The California Teachers Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- A scoping review of core outcome sets and their 'mapping' onto real-world data using prostate cancer as a case study.BMC medical research methodology · 2020Article
- Clinical Documentation to Predict Factors Associated with Urinary Incontinence Following Prostatectomy for Prostate Cancer.Research and reports in urology · 2020Article
- Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.Studies in health technology and informatics · 2019Article
- Extracting Patient-Centered Outcomes from Clinical Notes in Electronic Health Records: Assessment of Urinary Incontinence After Radical Prostatectomy.EGEMS (Washington, DC) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundElectronic health record (EHR) based research in oncology can be limited by missing data and a lack of structured data elements. Clinical research data warehouses for specific cancer types can enable the creation of more robust research cohorts.
methodsWe linked data from the Stanford University EHR with the Stanford Cancer Institute Research Database (SCIRDB) and the California Cancer Registry (CCR) to create a research data warehouse for prostate cancer. The database was supplemented with information from clinical trials, natural language processing of clinical notes and surveys on patient-reported outcomes.
results11,898 unique prostate cancer patients were identified in the Stanford EHR, of which 3,936 were matched to the Stanford cancer registry and 6153 in the CCR. 7158 patients with EHR data and at least one of SCIRDB and CCR data were initially included in the warehouse.
conclusionsA disease-specific clinical research data warehouse combining multiple data sources can facilitate secondary data use and enhance observational research in oncology.
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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.