{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007653856,0.0006545453,0.000461225,0.002265296,0.0002077192,0.0005254023,0.0004697796,0.0007142652,0.0008575089],"category_scores_gemma":[0.003941327,0.0001400388,0.0006875598,0.002448582,0.0001463827,0.0004997062,0.0006439697,0.0005340325,0.000472907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004728851,"about_ca_system_score_gemma":0.0004247872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01370458,"about_ca_topic_score_gemma":0.01600511,"domain_scores_codex":[0.99953,0.0001272093,0.00005383776,0.0001370758,0.0001004696,0.00005148196],"domain_scores_gemma":[0.9985562,0.0005472875,0.0002459064,0.0002053497,0.0003543887,0.00009088003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008235889,0.0007812477,0.7560904,0.0004525121,0.0005612281,0.0007859133,0.0001152107,0.07976124,0.002499308,0.0005022052,0.009793684,0.1478336],"study_design_scores_gemma":[0.0000598335,0.0006331106,0.5576214,0.0002327478,0.0002393564,0.0007553641,0.0006330345,0.4177949,0.003989334,0.001886442,0.01606557,0.00008891197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612958,0.001577734,0.007456993,0.0005880761,0.00009389305,0.00007799204,0.02627563,0.0003282109,0.002305638],"genre_scores_gemma":[0.9117779,0.0007038865,0.01637142,0.0001335229,0.00006644308,0.00008084283,0.06968853,0.00002537833,0.001152151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01370458,"threshold_uncertainty_score":0.02724957,"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."}}