{"id":"W4408664434","doi":"10.2196/62833","title":"Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach","year":2025,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Association (psychology); Risk factor; Cancer; Protective factor; Factor (programming language); Medicine; Oncology; Psychology; Computer science; Internal medicine; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.005221287,0.001024063,0.0008235439,0.002375371,0.0005381409,0.001241471,0.001489627,0.001207861,0.001735963],"category_scores_gemma":[0.01458446,0.0003482006,0.001557226,0.001059626,0.0007851435,0.0008393051,0.000988127,0.001379667,0.0002447352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279252,"about_ca_system_score_gemma":0.001768081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01258425,"about_ca_topic_score_gemma":0.008376686,"domain_scores_codex":[0.9977869,0.001385898,0.00007931815,0.0004030314,0.0002148248,0.0001300486],"domain_scores_gemma":[0.9903433,0.007848625,0.0008667042,0.0003891842,0.0004155721,0.0001366078],"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":[0.0002036885,0.0003526651,0.1247643,0.0001820148,0.0008100304,0.0004745231,0.0002862614,0.7882757,0.0005277472,0.0186096,0.00194006,0.06357341],"study_design_scores_gemma":[0.00001379282,0.00004496866,0.00623451,0.0000216115,0.00006097817,0.00006576589,0.00002769065,0.9749938,0.00009062408,0.01804794,0.0003835034,0.0000147944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2212074,0.001318745,0.770218,0.002956732,0.00007841644,0.0002596185,0.001592047,0.0005758853,0.001793147],"genre_scores_gemma":[0.9407603,0.0004765297,0.05633229,0.0002203311,0.000149135,0.0002352597,0.0008685251,0.00002608389,0.0009316523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01258425,"threshold_uncertainty_score":0.02761316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374000277521076,"score_gpt":0.268782725291892,"score_spread":0.2550427225166812,"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."}}