European guidelines for quality assurance in colorectal cancer screening and diagnosis: Overview and introduction to the full Supplement publication
Bibliographic record
Abstract
Population-based screening for early detection and treatment of colorectal cancer (CRC) and precursor lesions, using evidence-based methods, can be effective in populations with a significant burden of the disease provided the services are of high quality. Multidisciplinary, evidence-based guidelines for quality assurance in CRC screening and diagnosis have been developed by experts in a project co-financed by the European Union. The 450-page guidelines were published in book format by the European Commission in 2010. They include 10 chapters and over 250 recommendations, individually graded according to the strength of the recommendation and the supporting evidence. Adoption of the recommendations can improve and maintain the quality and effectiveness of an entire screening process, including identification and invitation of the target population, diagnosis and management of the disease and appropriate surveillance in people with detected lesions. To make the principles, recommendations and standards in the guidelines known to a wider professional and scientific community and to facilitate their use in the scientific literature, the original content is presented in journal format in an open-access Supplement of Endoscopy. The editors have prepared the present overview to inform readers of the comprehensive scope and content of the guidelines.
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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 | 0.044 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".