Key Issues in Reporting Common Cancer Specimens: Problems in Pathologic Staging of Colon Cancer
Bibliographic record
Abstract
Abstract Context. —Standardized pathologic assessment is a quality measure for cancer care. Objective. —Pathologic staging parameters and the clinically important stage-independent pathologic factors that pathologists find most problematic to evaluate in colorectal cancer resection specimens are reviewed. The objective of this review is to provide practical guidance for the practicing surgical pathologist. Data Sources. —Published literature related to the TNM staging system for colorectal cancer of the American Joint Committee on Cancer and the International Union Against Cancer and to stage-independent tissue-based prognostic factor evaluation was included in the review. Study Selection, Data Extraction, and Synthesis. —Published guidelines from authoritative sources and published peer-reviewed data related to colorectal cancer staging and pathologic prognostic factor assessment were included for consideration. The general and site-specific rules of application of the American Joint Committee on Cancer and International Union Against Cancer TNM staging system for the colorectum and the protocol for evaluation of colorectal cancer resection specimens of the Cancer Committee of the College of American Pathologists served as the basis for discussion and amplified with practical advice on specific application. Conclusions. —Standardization of pathologic evaluation of colorectal cancer resection specimens is essential for optimal patient care and is aided by the use of data-driven guidelines that are easily understood and consistently applied.
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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 | 0.401 | 0.587 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".