{"id":"W4237925769","doi":"10.21203/rs.2.11709/v1","title":"Meta-gene markers predict meningioma recurrence with high accuracy.","year":2019,"lang":"en","type":"preprint","venue":"Research Square","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Meningioma; Medicine; Internal medicine; Oncology; Computational biology; Biology; Radiology","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":[0.001241559,0.0006634049,0.0007174726,0.002131282,0.0003903528,0.0009844843,0.0004950242,0.0004481171,0.001663781],"category_scores_gemma":[0.00305261,0.0002016101,0.001289772,0.001963546,0.0001773001,0.0003515326,0.0006020315,0.000613709,0.0005943454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000619165,"about_ca_system_score_gemma":0.0006130218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003547267,"about_ca_topic_score_gemma":0.005982113,"domain_scores_codex":[0.9993427,0.0001602258,0.00004808287,0.0002341483,0.000132325,0.00008237868],"domain_scores_gemma":[0.9987981,0.0005804198,0.0002688501,0.0001314667,0.0001382902,0.00008283414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006328405,0.0001138495,0.9427692,0.0001385801,0.001882841,0.0001385918,0.0000521547,0.006244458,0.00799437,0.0001411643,0.001579549,0.03831224],"study_design_scores_gemma":[0.00005002419,0.0003721503,0.8750032,0.00006108017,0.002198702,0.0007962371,0.0001177332,0.108199,0.008285766,0.001351286,0.003521494,0.00004319525],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970243,0.003663758,0.0152611,0.0006448973,0.00007895055,0.00006601562,0.007838016,0.0004861116,0.001718006],"genre_scores_gemma":[0.9916514,0.0002752712,0.004131616,0.00005539989,0.00002902181,0.00002648222,0.003431758,0.00002893009,0.0003701775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003547267,"threshold_uncertainty_score":0.007053256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.144820265059354,"score_gpt":0.387958063591748,"score_spread":0.243137798532394,"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."}}