{"id":"W4402279651","doi":"10.26717/bjstr.2024.58.009130","title":"\"Breast Cancer Survival Analysis (ML Multimodal Comparative Study)\"","year":2024,"lang":"en","type":"article","venue":"Biomedical Journal of Scientific & Technical Research","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Cancer; Breast cancer; Oncology; Medicine; Internal medicine","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":[0.003133651,0.0004468628,0.0006245902,0.001398206,0.0004201955,0.0008409919,0.0008282418,0.0004633556,0.01429293],"category_scores_gemma":[0.005919767,0.0001462613,0.001745125,0.001559241,0.0002092884,0.0006086117,0.00154212,0.0006878027,0.002721271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005196428,"about_ca_system_score_gemma":0.000870163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003562265,"about_ca_topic_score_gemma":0.005145571,"domain_scores_codex":[0.9987184,0.0006250781,0.00008097127,0.0002852745,0.0001447282,0.0001455566],"domain_scores_gemma":[0.9982232,0.0006761124,0.0003381847,0.0003161045,0.0002251376,0.0002211465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01983205,0.001017755,0.6368445,0.001233198,0.004589499,0.0004901258,0.0004503566,0.002010002,0.001526159,0.002749316,0.121461,0.2077962],"study_design_scores_gemma":[0.001571546,0.004651761,0.8999667,0.0003472651,0.004160377,0.001849564,0.0008364928,0.01005853,0.001926804,0.002846632,0.07167011,0.0001141457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7233041,0.003753323,0.009171348,0.002204994,0.0004282184,0.0007975568,0.2454062,0.0007664142,0.01416784],"genre_scores_gemma":[0.8313867,0.0009083673,0.008454985,0.0009271826,0.0002767266,0.001553423,0.1439015,0.0001997966,0.01239141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01429293,"threshold_uncertainty_score":0.04781461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3484616906194856,"score_gpt":0.5493986518827239,"score_spread":0.2009369612632383,"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."}}