{"id":"W4417208861","doi":"10.1158/1557-3265.earlyonsetca25-ia002","title":"Abstract IA002: Early-Onset Colorectal Cancer as a Model for Precision Cancer Prevention","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Cancer Risks and Factors","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cancer prevention; Colorectal cancer; Cancer; Disease; Precision medicine; Microbiome; Aspirin; Disease prevention; Breast cancer","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002417259,0.0002501383,0.0007071529,0.0002996577,0.0003097157,0.00008537486,0.0003433755,0.0004430725,0.00155208],"category_scores_gemma":[0.001110506,0.0002095534,0.0004947753,0.0006955323,0.0004106621,0.0001481192,0.0001967853,0.001330907,0.00004156544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009288333,"about_ca_system_score_gemma":0.004937483,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01197074,"about_ca_topic_score_gemma":0.007896415,"domain_scores_codex":[0.996224,0.0001588287,0.000925994,0.000927522,0.0009234766,0.0008401272],"domain_scores_gemma":[0.9967191,0.001197778,0.000155369,0.0004853946,0.001026697,0.0004157249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.02119048,0.001120881,0.3583331,0.0007853559,0.0006713665,0.00001726427,0.0004816146,0.0007760058,0.002445919,0.0002309254,0.1425566,0.4713905],"study_design_scores_gemma":[0.0102576,0.001881419,0.8834751,0.003112784,0.0003990643,0.000002470236,0.0001675457,0.03309118,0.005403837,0.007702739,0.05397391,0.0005323653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834992,0.004694879,0.0004596653,0.005467647,0.00112948,0.00274344,0.0004130794,0.00007258397,0.00152003],"genre_scores_gemma":[0.9640097,0.0106222,0.0001570472,0.0005565225,0.0008157866,0.003715706,0.00003566172,0.00005402965,0.02003332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.525142,"threshold_uncertainty_score":0.9993606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3123093736101132,"score_gpt":0.6194755135353421,"score_spread":0.307166139925229,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}