{"id":"W7130946989","doi":"10.66108/mna.v4i1.64","title":"Predicting Colorectal Cancer Using Machine Learning and Worldwide Dietary Data","year":2025,"lang":"","type":"article","venue":"Machines and Algorithms","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Colorectal cancer; Artificial neural network; Disease; Cancer; Supervised learning; Big data; Unsupervised learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007386086,0.000508622,0.0007793971,0.0003288884,0.001021289,0.0002181517,0.0001984227,0.0002110109,0.000111483],"category_scores_gemma":[0.0003363131,0.0004637903,0.00007956043,0.0007231066,0.0002619281,0.0003049432,0.001136006,0.001086345,0.000001028021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001273708,"about_ca_system_score_gemma":0.000208182,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02348108,"about_ca_topic_score_gemma":0.00335764,"domain_scores_codex":[0.9971842,0.000191595,0.0005854211,0.00114794,0.0003153174,0.0005755033],"domain_scores_gemma":[0.9987409,0.0003306154,0.0002281911,0.000337871,0.0001016159,0.0002608469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001495707,0.00005319365,0.4846409,0.0004685729,0.0003573823,0.00003535099,0.0002936948,0.0001601329,0.00197932,0.000004239486,0.00003624877,0.5104752],"study_design_scores_gemma":[0.001981563,0.0008242289,0.0793708,0.001281313,0.0009489575,0.0001317809,0.0002962892,0.9106797,0.0003415726,0.0000478974,0.003719112,0.0003767842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8577899,0.1359907,0.00145518,0.001131901,0.001948088,0.0005264341,0.0002919399,0.0001585404,0.000707255],"genre_scores_gemma":[0.9825678,0.01155917,0.002218354,0.000243262,0.001267199,0.00002404508,0.0001482269,0.00005708668,0.001914814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9105195,"threshold_uncertainty_score":0.9997814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03178130870002038,"score_gpt":0.3285125481293665,"score_spread":0.2967312394293461,"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."}}