{"id":"W2996286890","doi":"10.1016/j.jval.2019.09.2321","title":"PPM4 COMPREHENSIVE GENOMIC PROFILING FOR NON-SMALL CELL LUNG CANCER (NSCLC): A HEALTH AND BUDGET IMPACT ANALYSIS","year":2019,"lang":"en","type":"article","venue":"Value in Health","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; William Osler Health System; Vancouver Coastal Health; Roche (Canada)","funders":"","keywords":"Medicine; Turnaround time; Genetic testing; Hotspot (geology); Profiling (computer programming); Oncology; Bioinformatics; Internal medicine; Computer science; Biology","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":[0.002614849,0.0004131818,0.0004062004,0.002381724,0.0002157101,0.000956135,0.0004872808,0.0003385564,0.003335634],"category_scores_gemma":[0.004757362,0.0002103234,0.0009288329,0.005952136,0.0002015288,0.0005632336,0.001155273,0.0004471648,0.0003308138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002666586,"about_ca_system_score_gemma":0.003298969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02861071,"about_ca_topic_score_gemma":0.04297742,"domain_scores_codex":[0.9981908,0.0007549566,0.00006670909,0.0001516061,0.0005736472,0.0002621896],"domain_scores_gemma":[0.9971188,0.0008526738,0.0006096874,0.00017971,0.0009623987,0.0002766274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002042931,0.0001773192,0.721746,0.0007954533,0.001722492,0.0003533575,0.0001982473,0.03225903,0.007345604,0.005388783,0.02050007,0.2074707],"study_design_scores_gemma":[0.00005482366,0.0004413694,0.9403107,0.0001955004,0.0008150545,0.0002748384,0.0004773444,0.01331446,0.004091987,0.002326534,0.03766166,0.00003572258],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8342029,0.005880743,0.01016056,0.01062087,0.00009411808,0.0003106547,0.1171251,0.0002126257,0.02139246],"genre_scores_gemma":[0.9602478,0.002094722,0.006904502,0.0005206521,0.00006987602,0.0001697023,0.02697361,0.00004235476,0.002976802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02861071,"threshold_uncertainty_score":0.05688834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608610984209335,"score_gpt":0.309570894603926,"score_spread":0.2934847847618326,"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."}}