ArticleJCO clinical cancer informatics2025
Assessing the Data Quality Dimensions of Surgical Oncology Cohorts in the
Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Assessing data quality of inflammatory bowel disease patients in theJAMIA open · 2026Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeCancer is a leading cause of morbidity and mortality in the United States. Mapping electronic health record (EHR) data to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) may standardize data structure and allow for multiple database oncology studies. However, the number of oncology studies produced with the OMOP CDM has been low. To investigate the discrepancy between the public health impact of cancer and the output of OMOP CDM clinical cancer studies, we evaluated (EHR) data quality of five surgical oncology cohorts in the
methodsWe selected procedure codes that were the basis of each phenotype. We used a data quality checklist to evaluate five domains systematically: conformance, completeness, concordance, plausibility, and temporality.
resultsMost phenotype-defining source codes were mapped to Current Procedural Terminology 4, which is an EHR standard. All cohorts had low concept prevalence. Most bivariate correlations between concepts were weak (⍴ ≤ 0.5). The small number of biomarkers available for use limited our plausibility analysis. The median time between biopsy and surgery varied across cohorts.
conclusionWe identified multiple data completeness issues, which limited the fitness for use evaluation. Also, using the OMOP CDM procedure concepts and mappings presented challenges for our study. Variable amounts of missingness in OMOP CDM surgical oncology data may affect the fitness for use of cancer data. Further research is warranted to improve the quality of that data.
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