ArticlePopulation health metrics2006
Consistency and accuracy of diagnostic cancer codes generated by automated registration: comparison with manual registration.
Article in Population health metrics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Bridging the data latency gap: automated extraction of genomic biomarkers from unstructured clinical documents to support real-world oncology data.Cancer causes & control : CCC · 2026Article
- OncoPT: long-context transformer models for in hospital tumor phenotype extraction from pathology reports.NPJ digital medicine · 2026Article
- Quality control on digital cancer registration.PloS one · 2022Article
- Systematic Review of Hospital Based Cancer Registries (HBCRs): Necessary Tool to Improve Quality of Care in Cancer PatientsAsian Pacific journal of cancer prevention : APJCP · 2017Article
- Building a model for disease classification integration in oncology, an approach based on the national cancer institute thesaurus.Journal of biomedical semantics · 2017Article
- Atmospheric fine particulate matter and breast cancer mortality: a population-based cohort study.BMJ open · 2016Article
- Fasting blood glucose and long-term prognosis of non-metastatic breast cancer: a cohort study.Breast cancer research and treatment · 2013Article
- Is hospital discharge administrative data an appropriate source of information for cancer registries purposes? Some insights from four Spanish registries.BMC health services research · 2010Article
- Descriptive epidemiology of selected birth defects, areas of Lombardy, Italy, 1999.Population health metrics · 2007Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutomated procedures are increasingly used in cancer registration, and it is important that the data produced are systematically checked for consistency and accuracy. We evaluated an automated procedure for cancer registration adopted by the Lombardy Cancer Registry in 1997, comparing automatically-generated diagnostic codes with those produced manually over one year (1997).
methodsThe automatically generated cancer cases were produced by Open Registry algorithms. For manual registration, trained staff consulted clinical records, pathology reports and death certificates. The social security code, present and checked in both databases in all cases, was used to match the files in the automatic and manual databases. The cancer cases generated by the two methods were compared by manual revision.
resultsThe automated procedure generated 5027 cases: 2959 (59%) were accepted automatically and 2068 (41%) were flagged for manual checking. Among the cases accepted automatically, discrepancies in data items (surname, first name, sex and date of birth) constituted 8.5% of cases, and discrepancies in the first three digits of the ICD-9 code constituted 1.6%. Among flagged cases, cancers of female genital tract, hematopoietic system, metastatic and ill-defined sites, and oropharynx predominated. The usual reasons were use of specific vs. generic codes, presence of multiple primaries, and use of extranodal vs. nodal codes for lymphomas. The percentage of automatically accepted cases ranged from 83% for breast and thyroid cancers to 13% for metastatic and ill-defined cancer sites.
conclusionSince 59% of cases were accepted automatically and contained relatively few, mostly trivial discrepancies, the automatic procedure is efficient for routine case generation effectively cutting the workload required for routine case checking by this amount. Among cases not accepted automatically, discrepancies were mainly due to variations in coding practice.
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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.