{"id":"W2996421917","doi":"10.1016/j.jval.2019.09.2320","title":"PPM3 GENOMIC SCREENING COSTS IN MODELLING: A PENALTY FOR INNOVATION?","year":2019,"lang":"en","type":"article","venue":"Value in Health","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Roche (Canada); Memorial University of Newfoundland","funders":"","keywords":"Medicine; Oncology; Cost-effectiveness analysis; Internal medicine; Cost effectiveness; Risk analysis (engineering)","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":[0.008588738,0.000717001,0.001112051,0.0007148924,0.0006378307,0.003820316,0.002622108,0.00442911,0.01758724],"category_scores_gemma":[0.1004138,0.0007685123,0.001038048,0.001173608,0.0019746,0.00717101,0.001865918,0.005620468,0.0009649553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002060022,"about_ca_system_score_gemma":0.001864582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009468498,"about_ca_topic_score_gemma":0.006711482,"domain_scores_codex":[0.995884,0.002520057,0.000198329,0.0004168721,0.0006397341,0.0003409061],"domain_scores_gemma":[0.9487126,0.04320076,0.001625003,0.003689739,0.001901145,0.0008707664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004845535,0.0001162056,0.009925695,0.0003087046,0.0001570961,0.0003667499,0.0003659956,0.5560886,0.000447075,0.3632132,0.01203683,0.05648927],"study_design_scores_gemma":[0.00008434587,0.00009976765,0.002902083,0.0002208825,0.00008003769,0.000249321,0.0001976788,0.5425547,0.0007553206,0.4408855,0.01190134,0.00006904498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.216251,0.003418183,0.6135176,0.06722559,0.00182729,0.0002060295,0.002937615,0.0008147191,0.09380194],"genre_scores_gemma":[0.9563449,0.0005184353,0.02854004,0.001825266,0.0003040863,0.0001452191,0.0004302525,0.0002585068,0.01163328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01758724,"threshold_uncertainty_score":0.05883521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5644246595470902,"score_gpt":0.4469534415128759,"score_spread":0.1174712180342143,"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."}}