Colonoscopy and Sigmoidoscopy Use among the Average-Risk Population for Colorectal Cancer: A Systematic Review and Trend Analysis
Bibliographic record
Abstract
Monitoring population-level colonoscopy and sigmoidoscopy use is crucial to estimate the future burden of colorectal cancer and guide screening efforts. We conducted a systematic literature search on colonoscopy and sigmoidoscopy use, published between November 2016 and December 2018 in the databases PubMed and Web of Science to update previous reviews and analyze time trends for various countries. In addition, we used data from the German and European Health Interview Surveys and the National Health Interview Survey to explore recent time trends for Germany and the US, respectively. The literature search yielded 23 new articles: fourteen from the US and nine from Australia, Canada, England, Germany, Saudi Arabia, and South Korea. Colonoscopy use within 10 years was highest and, apart from the youngest age groups eligible for colorectal cancer screening, kept increasing to levels close to 60% in the US and Germany. A recent steep increase was also observed for South Korea. Limited data were available on sigmoidoscopy use; regional studies from the US suggest that sigmoidoscopy has become rarely used. Despite high uptake and ongoing increase in the US, Germany, and South Korea, use of colonoscopy and sigmoidoscopy has either remained low or essentially unknown for the majority of countries.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".