Definition and taxonomy of interval colorectal cancers: a proposal for standardising nomenclature
Bibliographic record
Abstract
OBJECTIVE: Interval colorectal cancers (interval CRCs), that is, cancers occurring after a negative screening test or examination, are an important indicator of the quality and effectiveness of CRC screening and surveillance. In order to compare incidence rates of interval CRCs across screening programmes, a standardised definition is required. Our goal was to develop an internationally applicable definition and taxonomy for reporting on interval CRCs. DESIGN: Using a modified Delphi process to achieve consensus, the Expert Working Group on interval CRC of the Colorectal Cancer Screening Committee of the World Endoscopy Organization developed a nomenclature for defining and characterising interval CRCs. RESULTS: We define an interval CRC as a "colorectal cancer diagnosed after a screening or surveillance exam in which no cancer is detected, and before the date of the next recommended exam". Guidelines and principles for describing and reporting on interval CRCs are provided, and clinical scenarios to demonstrate the practical application of the nomenclature are presented. CONCLUSIONS: The Working Group on interval CRC of the World Endoscopy Organization endorses adoption of this standardised nomenclature. A standardised nomenclature will facilitate benchmarking and comparison of interval CRC rates across programmes and regions.
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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 | 0.056 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".