A Review of Current Challenges in Colorectal Cancer Reporting
Bibliographic record
Abstract
CONTEXT.—: Pathologic assessment of colorectal cancer resection specimens plays an important role in postsurgical management and prognostication in patients with colorectal cancer. Challenges exist in the evaluation and reporting of these specimens, either because of difficulties in applying existing guidelines or related to newer concepts. OBJECTIVE.—: To address challenging areas in colorectal cancer pathology and to provide an overview of the literature, current guidelines, and expert recommendations for the handling of colorectal cancer resection specimens in everyday practice. DATA SOURCES.—: PubMed (US National Library of Medicine, Bethesda, Maryland) literature review; reporting protocols of the College of American Pathologists, the Royal College of Pathologists of the United Kingdom, and the Japanese Society for Cancer of the Colon and Rectum; and classification manuals of the American Joint Committee on Cancer and the Union for International Cancer Control. CONCLUSIONS.—: This review has addressed issues and challenges affecting quality of colorectal cancer pathology reporting. High-quality pathology reporting is essential for prognostication and management of patients with colorectal cancer.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".