{"id":"W3100061827","doi":"10.2196/19069","title":"Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"West China Hospital, Sichuan University; Sichuan University; Department of Science and Technology of Sichuan Province; University of Cambridge","keywords":"Breast cancer; Computer science; Machine learning; Population; Cohort; Medicine; Algorithm; Artificial intelligence; Cancer; Internal medicine; Environmental health","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":[0.01504697,0.001125521,0.001782598,0.002205793,0.0007983529,0.001058959,0.001956384,0.001122036,0.001019525],"category_scores_gemma":[0.01365639,0.0003665599,0.001652598,0.001094715,0.0005633563,0.0006888253,0.001282977,0.001248143,0.0002136609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0017741,"about_ca_system_score_gemma":0.00321322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02475701,"about_ca_topic_score_gemma":0.01193425,"domain_scores_codex":[0.9982129,0.0008981028,0.0001449701,0.0004021708,0.0001774998,0.0001643803],"domain_scores_gemma":[0.9924384,0.004125873,0.0005369498,0.001131139,0.001347998,0.0004195923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009501015,0.0009027483,0.6998065,0.000130249,0.001154797,0.0006128314,0.0002673489,0.2305832,0.0006438608,0.0005984828,0.002382116,0.06196773],"study_design_scores_gemma":[0.0001355272,0.0002353323,0.06796277,0.00002884819,0.0002042188,0.0001688481,0.00009298555,0.9300809,0.000306209,0.0004466444,0.0003108639,0.0000268392],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988186,0.0003206669,0.01030061,0.0001909978,0.00002041443,0.0001241269,0.0004879939,0.0001250881,0.0002441973],"genre_scores_gemma":[0.9880095,0.0001691795,0.009174647,0.00007241456,0.00002000187,0.0001677802,0.002066691,0.00001442618,0.0003052978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02475701,"threshold_uncertainty_score":0.07957691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01846788515059048,"score_gpt":0.2908997767887492,"score_spread":0.2724318916381587,"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."}}