{"id":"W2740877401","doi":"10.1158/1538-7445.am2017-lb-102","title":"Abstract LB-102: Landscape analysis of the initial data release from AACR Project GENIE","year":2017,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Cancer; Clinical trial; Interoperability; Citation; Medicine; Precision medicine; Data science; Computer science; Internal medicine; Library science; World Wide Web; Pathology","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":[0.009657329,0.000455207,0.0006947986,0.003327399,0.0006920812,0.002773125,0.00133759,0.001130939,0.004056846],"category_scores_gemma":[0.03299234,0.0003588762,0.0009852584,0.005930864,0.0006772565,0.001508226,0.001598743,0.001032962,0.001268013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002822975,"about_ca_system_score_gemma":0.001442566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0550207,"about_ca_topic_score_gemma":0.04369797,"domain_scores_codex":[0.9915569,0.003337578,0.0004179743,0.001079752,0.003015878,0.0005919588],"domain_scores_gemma":[0.9756073,0.01404151,0.001809139,0.002847302,0.005109447,0.0005852529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003372841,0.00140275,0.3639382,0.00139862,0.001315466,0.002323395,0.002496588,0.1328528,0.008349305,0.01983633,0.2982284,0.1644853],"study_design_scores_gemma":[0.000424598,0.0008701627,0.6033958,0.0002873393,0.0002343634,0.001156614,0.004130958,0.2760773,0.005574615,0.006422566,0.1011826,0.0002429895],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8142376,0.0004652325,0.009319503,0.005601079,0.00009438008,0.0003647484,0.1545098,0.002273207,0.01313447],"genre_scores_gemma":[0.722038,0.0001972046,0.01697154,0.0006136567,0.00004218515,0.0003901052,0.2539166,0.001001822,0.004829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0550207,"threshold_uncertainty_score":0.1094009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1567032919688904,"score_gpt":0.4564489004946577,"score_spread":0.2997456085257674,"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."}}