{"id":"W2947287592","doi":"10.1200/jco.2019.37.15_suppl.3561","title":"Utilization and reach of the Fight Colorectal Cancer Late Stage MSS CRC Clinical Trial Finder.","year":2019,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Clinical trial; Colorectal cancer; Cancer; Stage (stratigraphy); Oncology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.03292352,0.0009883879,0.001680063,0.008018697,0.000959271,0.006099389,0.001939761,0.0025534,0.1272781],"category_scores_gemma":[0.1832005,0.001051306,0.001804828,0.00468576,0.0005535909,0.006331225,0.005227279,0.002183156,0.04971439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002105824,"about_ca_system_score_gemma":0.005919758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002016848,"about_ca_topic_score_gemma":0.005121654,"domain_scores_codex":[0.9865128,0.006529803,0.002308538,0.001606363,0.002517686,0.0005248032],"domain_scores_gemma":[0.8416856,0.1022502,0.01833843,0.009310644,0.01091396,0.01750124],"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":[0.003677617,0.0001318703,0.007197916,0.003392471,0.0003223692,0.0001203472,0.0003478917,0.0004401821,0.0004216196,0.003071198,0.7335027,0.2473738],"study_design_scores_gemma":[0.005113894,0.001208456,0.01925771,0.005433857,0.0007717762,0.0008558163,0.0002972552,0.00541546,0.001037637,0.01289046,0.9474249,0.0002927263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03396921,0.03311214,0.03659064,0.09761371,0.004797332,0.005761385,0.4659723,0.1298816,0.1923018],"genre_scores_gemma":[0.2092341,0.0166279,0.2230785,0.0457066,0.007186167,0.01493155,0.3556997,0.03593398,0.09160151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1272781,"threshold_uncertainty_score":0.4257877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1663466852735201,"score_gpt":0.5275581294484417,"score_spread":0.3612114441749216,"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."}}