{"id":"W4409168442","doi":"10.21203/rs.3.rs-5731247/v1","title":"Exploring the Potential of Machine Learning in Gastric Cancer: Prognostic Biomarkers, Subtyping, and Stratification","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gastric Cancer Management and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Subtyping; Stratification (seeds); Risk stratification; Cancer; Medicine; Oncology; Computer science; Internal medicine; Biology; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01112422,0.0009382077,0.001001899,0.001596273,0.0003833735,0.003468111,0.0006897594,0.0009601507,0.002569758],"category_scores_gemma":[0.05344622,0.0002743686,0.000704264,0.001596955,0.001245342,0.002691893,0.00150132,0.002091902,0.0004595332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006858906,"about_ca_system_score_gemma":0.00226438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001750758,"about_ca_topic_score_gemma":0.001265016,"domain_scores_codex":[0.9968867,0.002394692,0.00007288801,0.0002355011,0.0002992002,0.000111006],"domain_scores_gemma":[0.9679087,0.02803424,0.001146457,0.001254435,0.00112124,0.0005350411],"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":[0.001509761,0.0005670115,0.1811382,0.001234507,0.001616204,0.0003780044,0.0004703686,0.07663646,0.001615427,0.08969814,0.03026284,0.6148731],"study_design_scores_gemma":[0.0001408867,0.0003377561,0.01781493,0.0003680694,0.0003517576,0.0001683463,0.000295907,0.4323353,0.001344621,0.5373887,0.009398359,0.00005535067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3792261,0.05881509,0.3709446,0.1631795,0.002930373,0.0001926138,0.003612568,0.001095121,0.02000393],"genre_scores_gemma":[0.9346859,0.009805209,0.04801854,0.001435586,0.002351939,0.00006731069,0.0007148236,0.00009403417,0.002826534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01112422,"threshold_uncertainty_score":0.05883121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1076701823875583,"score_gpt":0.3809137903616962,"score_spread":0.2732436079741379,"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."}}