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Record W4242036144 · doi:10.5858/134.5.663

Electronic Pathology Reporting: Digitizing the College of American Pathologists Cancer Checklists

2010· letter· en· W4242036144 on OpenAlexaboutno aff
Monica E. de Baca, John F. Madden, Mary Kennedy

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2010
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistAccreditationMedicineCancerCommissionFamily medicineHealth careMedical physicsMEDLINEMedical educationPathologyPsychologyInternal medicineBusinessPolitical science

Abstract

fetched live from OpenAlex

The recent editorial1 by Mahul B. Amin, MD, reports on the College of American Pathologists (CAP) Cancer Committee's latest release of the CAP Cancer Protocols and Checklists, and offers perspective on the history and importance of standardized, structured pathology reporting for effective cancer care. The CAP cancer checklists (CCs) are recognized as the gold standard for pathology reporting of cancer cases. Developed by the CAP Cancer Committee in collaboration with pathology, surgery, oncology, and radiation therapy experts, the synoptic checklist format ensures consistent reporting of scientifically validated elements and enables the medical community to retrieve, share, compare, and research clinical data for improved patient care. Use of the CAP cancer checklists has become widespread throughout the United States. The American College of Surgeons Commission on Cancer requires the use of the essential data elements in the cancer checklists for accreditation. In addition, they are widely used in Canada (eg, Cancer Care Ontario [CCO]) and have become well known in many other countries.Historically, the CAP cancer checklists had been paper based. As health information technology advanced, it became evident that an electronic version of the checklists was needed. Several years ago, CAP began to offer a SNOMED CT (Systematized Nomenclature of Medicine–Clinical Terms)–encoded checklist version in a database format to software developers. With increased complexity of reporting, dynamic changes in the checklist content, and the need to support a broad range of rapidly evolving health information platforms, CAP formed the Pathology Electronic Reporting Taskforce (PERT) in 2005 with funding support from the Centers for Disease Control and Prevention (CDC). It is composed of CAP member experts in cancer and information technology, and also currently includes representatives from the North American Association of Central Cancer Registries, the American Joint Commission on Cancer (AJCC) and its Collaborative Staging initiative, the US Department of Health & Human Services Office of the Assistant Secretary for Planning and Evaluation, the CDC, the Canadian Partnership Against Cancer, CCO, CAP staff, and other specialists. PERT's mission is to advance the implementation of the CAP cancer checklists by using health information technology. This goal is one of the mandates of the PERT's parent department within the CAP, the Diagnostic Intelligence and Health Information Technology (DIHIT), which aims to improve patient care and extend the role of the pathologist by developing standards and electronic tools for pathology practice. By integrating the CC content with other relevant electronic reporting standards for public health data collection and clinical care (including SNOMED CT, LOINC, caBIG, HL7, and others), PERT aims to make the cancer committee's work accessible to an ever-wider audience, and to facilitate transmission and storage of CC-compliant patient reports.In January 2009, the PERT-developed electronic cancer checklists (eCCs) were first released in an eXtensible Markup Language (XML) format. This release format parses the paper-based checklists into datasets suitable for incorporation into software products and can be used to standardize the electronic collection and transmission of CC data. XML was chosen for its universal acceptance, ease of use, and its ability to facilitate the sharing of structured data across disparate systems, ranging from laboratory information systems and cancer registry systems to comprehensive electronic health records systems and future personal health records. In addition to patient care, we anticipate its increased use in public health surveillance, research, tissue banking, and quality improvement.An update in this format in December 2009 encoded the October 2009 CAP Cancer Protocols and Checklists and incorporated the AJCC 7th edition staging criteria. Subsequent releases will follow in the coming months as more cancer protocols are released by the CAP Cancer Committee. SNOMED CT encoding for histology and tumor site will be included in this release; subsequently, additional checklist sections will receive SNOMED CT mappings.In the first quarter of 2010, PERT will offer a preview release of its next-generation XML format incorporating several of the newly published 2009 CAP Cancer Committee Cancer Checklists. This will introduce PERT's new inclusive framework for creation and distribution of structured (sometimes referred to as “synoptic”) medical diagnostic reports. Initially designed for cancer reporting in pathology, this framework is intended to be scalable to other medical specialties, such as radiology, for their structured reporting needs. The cornerstone design concept is to maintain a modular, loosely coupled relationship among the user interface, the underlying data model and model extensions, the sharable semantics (ie, terminology binding), and the transport format. The design framework will incorporate the following components:The time has come to move from paper to electronic reporting in pathology. Electronic reporting tools will dramatically facilitate incorporation of cancer checklist information into the health care workflow. The monumental accomplishments of the CAP Cancer Committee in producing the revised 2009 checklists move us closer to that goal. Conversion of the CAP Cancer Committee's content into the electronic cancer checklist versions relies on close consultation with the committee members, voluntary participation of numerous experts from the pathology, registrar, epidemiology, and IT communities, and support from the CAP's DIHIT department. We hope that by assisting in the accurate determination of stage, facilitating data transmission, and increasing patient safety, the eCCs will assist the community of pathologists in making an ever-growing impact on cancer care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.011
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.301
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2010
Admission routes1
Has abstractyes

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