CanScreen5, a global repository for breast, cervical and colorectal cancer screening programs
Bibliographic record
Abstract
Abstract The CanScreen5 project is a global cancer screening data repository that aims to report the status and performance of breast, cervical and colorectal cancer screening programs using a harmonized set of criteria and indicators. Data collected mainly from the Ministry of Health in each country underwent quality validation and ultimately became publicly available through a Web-based portal. Until September 2022, 84 participating countries reported data for breast ( n = 57), cervical ( n = 75) or colorectal ( n = 51) cancer screening programs in the repository. Substantial heterogeneity was observed regarding program organization and performance. Reported screening coverage ranged from 1.7% (Bangladesh) to 85.5% (England, United Kingdom) for breast cancer, from 2.1% (Côte d'Ivoire) to 86.3% (Sweden) for cervical cancer, and from 0.6% (Hungary) to 64.5% (the Netherlands) for colorectal cancer screening programs. Large variability was observed regarding compliance to further assessment of screening programs and detection rates reported for precancers and cancers. A concern is lack of data to estimate performance indicators across the screening continuum. This underscores the need for programs to incorporate quality assurance protocols supported by robust information systems. Program organization requires improvement in resource-limited settings, where screening is likely to be resource-stratified and tailored to country-specific situations.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".