Role of medico-administrative database in the selection of the target population in colorectal cancer screening program
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) screening in average-risk populations requires filtering a target population based on medical information in population-based CRC screening programs (CRCSP). This study describes the level of consensus in medical exclusion practice and the role of the medico-administrative databases (MADB) in accurately targeting the eligible individuals for CRCSP screening campaigns. Design: The descriptive study combined a cross-sectional survey and a non-systematic literature review. Methods: A cross-sectional survey was conducted among CRCSPs worldwide. Information was collected on the use of MADB for identifying consensus-based exclusion criteria (applied by >50% of CRCSPs). When a MADB was used, the study assessed whether the definition (code lists, medical terminologies) of the exclusion criteria was available. These definitions were compared between programs to evaluate the degree of consensus. Results: In all, 20 out of the 31 CRCSPs (Australia, England, Manitoba, Ontario, Washington State, 26 European countries) participating in the survey implemented medical exclusions. Five consensus-based exclusion criteria were identified (personal history of CRC, inflammatory bowel disease, adenoma, recent colonoscopy, genetic risk). However, these criteria were not uniformly defined in MADBs (i.e., CRC phenotype includes ICD-10 codes C18-C21 in Catalonia, while the C21 code was excluded elsewhere). Furthermore, although the MADBs exist and contain relevant information, they remain inaccessible to screening management structures in some countries (e.g., in France). Conclusion: The number of consensus-based criteria was limited, and they were the least nuanced, likely because they are easier to collect using the current CRCSPs management resources. These consensual criteria can be queried in most MADBs. However, the use of MADBs was not standardized across programs for various reasons (absence of a database, unavailability of information in the database when it exists, inaccessibility of the database when it exists), limiting comparability between them. Standardizing the five consensus criteria across all programs would only be effective if the disparity caused by systemic failures in the organization of each program was controlled.
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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 | 0.052 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".