{"id":"W4296078717","doi":"10.21203/rs.3.rs-2031672/v1","title":"Machine Learning-based Colorectal Cancer Prediction using Global Dietary Data","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Artificial intelligence; Colorectal cancer; Psychological intervention; Medicine; Medical diagnosis; Computer science; Artificial neural network; Disease; Cancer; Internal medicine; Pathology","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":[0.001128584,0.0006668888,0.0005732868,0.001289667,0.0001310764,0.0005417807,0.0003150222,0.000366384,0.00131193],"category_scores_gemma":[0.005013623,0.0001375648,0.0005711844,0.00109445,0.0001529203,0.0003898335,0.0003792791,0.0004318836,0.0003483506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004270605,"about_ca_system_score_gemma":0.0004506484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0100084,"about_ca_topic_score_gemma":0.007475495,"domain_scores_codex":[0.9996463,0.0001508343,0.00002033846,0.0001159307,0.00003768976,0.00002890767],"domain_scores_gemma":[0.9978107,0.001435376,0.0002137786,0.00016476,0.0003046677,0.00007087496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009522125,0.0005155149,0.3616285,0.0002182497,0.0004859202,0.0002069978,0.00005478962,0.4826507,0.001495454,0.000567601,0.004322058,0.1469019],"study_design_scores_gemma":[0.00003208161,0.0001648645,0.04888766,0.00002857836,0.00006108887,0.00005595452,0.00003797365,0.9475881,0.001013015,0.001423814,0.0006935945,0.00001329124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451052,0.0008199493,0.04620174,0.0004625158,0.00007421568,0.00009561234,0.004758622,0.000483685,0.001998492],"genre_scores_gemma":[0.972329,0.0001820484,0.02093563,0.00004588118,0.00004172569,0.00005134378,0.005280538,0.00001973414,0.001114005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0100084,"threshold_uncertainty_score":0.01990026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1752358765748155,"score_gpt":0.454088766575883,"score_spread":0.2788528900010675,"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."}}