{"id":"W4409965136","doi":"10.1186/s12885-025-14204-x","title":"Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification","year":2025,"lang":"en","type":"article","venue":"BMC Cancer","topic":"Gastric Cancer Management and Outcomes","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Cancer Research Center, Tehran University of Medical Sciences; Tehran University of Medical Sciences and Health Services","keywords":"Subtyping; Surgical oncology; Medicine; Stratification (seeds); Risk stratification; Oncology; Cancer; Internal medicine; Computational biology; Biology; Computer science","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":[0.006240386,0.0007658346,0.0006443543,0.001732512,0.0002589826,0.001558857,0.0005107012,0.0005763933,0.0009872288],"category_scores_gemma":[0.01208915,0.0001964282,0.0008538638,0.00114509,0.0006391043,0.001145636,0.0008181226,0.0009795635,0.0002635558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006243167,"about_ca_system_score_gemma":0.001148073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001136681,"about_ca_topic_score_gemma":0.00138769,"domain_scores_codex":[0.9981494,0.001343298,0.00006758544,0.0001208547,0.0002215057,0.00009731339],"domain_scores_gemma":[0.9938264,0.004821365,0.0005564341,0.0002276306,0.0003931544,0.0001748084],"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":[0.0008007991,0.0003435035,0.5080715,0.0006113523,0.0009153619,0.0002255327,0.0002746925,0.06161255,0.002478509,0.003452406,0.00300472,0.418209],"study_design_scores_gemma":[0.00009949566,0.001688664,0.1725219,0.0006403521,0.0007959133,0.0005747907,0.0004593522,0.7617541,0.00389923,0.05124313,0.006212617,0.0001104507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8168549,0.03171638,0.1349663,0.01045415,0.0002962909,0.0002396272,0.0007925753,0.0003655361,0.004314155],"genre_scores_gemma":[0.9809459,0.003435114,0.01457588,0.0002406619,0.0001844444,0.00005405968,0.0002312025,0.0000110019,0.0003217477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006240386,"threshold_uncertainty_score":0.03300267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419170229673327,"score_gpt":0.2915249625757961,"score_spread":0.2473332602790628,"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."}}