{"id":"W6958375940","doi":"10.6084/m9.figshare.26563797.v1","title":"Additional file 1 of Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; Western University; University of Manitoba","funders":"","keywords":"Python (programming language); Breast cancer; Lasso (programming language); Radiogenomics; Distributed File System; Set (abstract data type); Table (database); R package; Radiomics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001378748,0.001428386,0.001511302,0.001793071,0.0007048606,0.001687028,0.002203787,0.001334147,0.8517458],"category_scores_gemma":[0.02170878,0.0005701836,0.001052997,0.00239956,0.0002725322,0.001510175,0.0009228662,0.001180382,0.1671807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008015384,"about_ca_system_score_gemma":0.001426338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004540231,"about_ca_topic_score_gemma":0.009561792,"domain_scores_codex":[0.9993647,0.00009600168,0.0001022738,0.0002187051,0.0001321361,0.00008633006],"domain_scores_gemma":[0.988297,0.008926833,0.0005637957,0.0009384549,0.0009465268,0.0003273531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005806388,0.0001715846,0.006232245,0.003549,0.0001388173,0.0001811597,0.00006000174,0.0009250777,0.0003719233,0.000616784,0.9663344,0.02083842],"study_design_scores_gemma":[0.009404964,0.001014961,0.08655616,0.005193701,0.0007224978,0.003157535,0.0006606684,0.01258522,0.00448009,0.02206916,0.8537615,0.0003935441],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.000361128,0.00003347642,0.0005596554,0.000079404,0.00003031107,0.00004966042,0.9980336,0.0004454456,0.0004073114],"genre_scores_gemma":[0.01266096,0.0001704214,0.005760674,0.0005073429,0.0001455727,0.001610448,0.97066,0.001171395,0.007313065],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8517458,"threshold_uncertainty_score":0.2114665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008163429113300277,"score_gpt":0.2796420629166919,"score_spread":0.2714786338033917,"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."}}