{"id":"W2088293391","doi":"10.1002/cncr.24343","title":"Conclusions and reflections","year":2009,"lang":"en","type":"article","venue":"Cancer","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Nomogram; Medicine; Outcome (game theory); Clinical trial; Disease; Risk stratification; Intensive care medicine; Cancer; Oncology; Internal medicine; Mathematical economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003815743,0.00003204702,0.00006989141,0.00002429142,0.00007346373,0.000006069623,0.00001331999,0.00001867682,0.0001509095],"category_scores_gemma":[0.00005396838,0.00002521707,0.00001448677,0.00005772024,0.00003279639,0.00001540754,0.000006765948,0.0001293115,0.00000363362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002178204,"about_ca_system_score_gemma":0.00003292121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002349919,"about_ca_topic_score_gemma":0.000001804189,"domain_scores_codex":[0.9997509,0.000004840544,0.0000468156,0.00007531841,0.00004879695,0.00007335366],"domain_scores_gemma":[0.999818,0.00001068413,0.0000103127,0.00005708321,0.00001663411,0.00008733735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004799625,0.00008153127,0.02205364,0.00002759488,0.0000448986,0.00005692171,0.000875232,0.00003812632,0.0911361,0.008156818,0.05333144,0.8241497],"study_design_scores_gemma":[0.001326484,0.0002256868,0.1615884,0.0001568332,0.00009627783,0.0001719382,0.00007904254,0.004115512,0.0006724973,0.002038578,0.8294024,0.0001263488],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7728416,0.004771466,0.002970673,0.1232179,0.0004505423,0.0001953778,0.00000244427,0.0001503342,0.09539969],"genre_scores_gemma":[0.9856482,0.0005537505,0.001350284,0.009391975,0.0002423232,0.00000393139,0.000001657649,0.000004171725,0.002803723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8240234,"threshold_uncertainty_score":0.1652353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997036788727629,"score_gpt":0.3930172524540528,"score_spread":0.3730468845667765,"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."}}