From colorectal cancer pattern to the characterization of individuals at risk: Picture for genetic research in Latin America
Bibliographic record
Abstract
Colorectal cancer (CRC) is one of the most common cancers in Latin America and the Caribbean, with the highest rates reported for Uruguay, Brazil and Argentina. We provide a global snapshot of the CRC patterns, how screening is performed, and compared/contrasted to the genetic profile of Lynch syndrome (LS) in the region. From the literature, we find that only nine (20%) of the Latin America and the Caribbean countries have developed guidelines for early detection of CRC, and also with a low adherence. We describe a genetic profile of LS, including a total of 2,685 suspected families, where confirmed LS ranged from 8% in Uruguay and Argentina to 60% in Peru. Among confirmed LS, path_MLH1 variants were most commonly identified in Peru (82%), Mexico (80%), Chile (60%), and path_MSH2/EPCAM variants were most frequently identified in Colombia (80%) and Argentina (47%). Path_MSH6 and path_PMS2 variants were less common, but they showed important presence in Brazil (15%) and Chile (10%), respectively. Important differences exist at identifying LS families in Latin American countries, where the spectrum of path_MLH1 and path_MSH2 variants are those most frequently identified. Our findings have an impact on the evaluation of the patients and their relatives at risk for LS, derived from the gene affected. Although the awareness of hereditary cancer and genetic testing has improved in the last decade, it is remains deficient, with 39%-80% of the families not being identified for LS among those who actually met both the clinical criteria for LS and showed MMR deficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".