Elements of screening and early diagnosis of lower GI neoplastic lesions – an overview
Bibliographic record
Abstract
Dobrow's 12 consolidated principles were applied to evaluate the extent to which colorectal cancer (CRC) screening programmes align with these established screening principles. The principles within the first domain - disease and conditions - are fully met. CRC represents a significant global public health burden with a well-understood natural history, including a long preclinical phase. These features make CRC suitable for population-based screening. Current guidelines recommend screening average-risk individuals, with starting ages as early as 45 years and stopping ages up to 85 years in some programs, although most programs target those aged 50-75 years. For Dobrow's second domain - test or intervention - we found that established CRC screening methods (faecal immunochemical testing (FIT), colonoscopy, and flexible sigmoidoscopy) have known and acceptable sensitivity and specificity. These tests have clear thresholds for interpretation and follow-up and are associated with reductions in both CRC incidence and mortality, confirming their clinical effectiveness in organised programmes. Dobrow's third domain - programme or system - addresses how screening programmes should be structured, implemented, and integrated into health systems. Unlike the other domains, this is fully achieved in only a small minority of programmes. While implementation may vary from country to country, the principles provide benchmarks for well-organised programmes. CRC screening is generally cost-effective and widely accepted by patients, providers, and society. Successful programmes require dedicated teams, infrastructure, and systems for coordination and quality assurance. Before implementation, a CRC screening initiative must meet, or have a clear plan to meet 12 principles. In summary, robust infrastructure and governance are essential for organised CRC screening programmes to align with the Dobrow principles and achieve long-term success.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".