Comparing Colorectal Cancer Screening Outcomes in the International Cancer Screening Network: A Consortium Proposal
Bibliographic record
Abstract
Randomized trials have shown that stool testing for occult blood and flexible sigmoidoscopy reduce colorectal cancer (CRC) incidence and mortality, and based on observational evidence of its effectiveness, colonoscopy is also frequently used as a screening modality. 1 International Agency for Research on CancerColorectal cancer screening. Volume 17. Geneva: WHO Press, 2019. http://publications.iarc.fr/573 Google Scholar Opportunistic and organized CRC screenings are now available in many countries, presenting opportunities for knowledge exchange and data sharing that could improve the delivery of screening and further reduce CRC morbidity and mortality worldwide. However, it is challenging to reliably compare CRC screening programs or to build models to predict the long-term outcomes of programs that adopt very diverse parameters.
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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 | 0.435 | 0.460 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".