Cancer Biomarkers: The Role of Structured Data Reporting
Bibliographic record
Abstract
CONTEXT: The College of American Pathologists has been producing cancer protocols since 1986 to aid pathologists in the diagnosis and reporting of cancer cases. Many pathologists use the included cancer case summaries as templates for dictation/data entry into the final pathology report. These summaries are now available in a computer-readable format with structured data elements for interoperability, packaged as "electronic cancer checklists." Most major vendors of anatomic pathology reporting software support this model. OBJECTIVES: To outline the development and advantages of structured electronic cancer reporting using the electronic cancer checklist model, and to describe its extension to cancer biomarkers and other aspects of cancer reporting. DATA SOURCES: Peer-reviewed literature and internal records of the College of American Pathologists. CONCLUSIONS: Accurate and usable cancer biomarker data reporting will increasingly depend on initial capture of this information as structured data. This process will support the standardization of data elements and biomarker terminology, enabling the meaningful use of these datasets by pathologists, clinicians, tumor registries, and patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".