{"id":"W2986598556","doi":"10.1007/s40258-019-00533-z","title":"Forecasting Ontario Oncology Drug Expenditures: A Hybrid Approach to Improving Accuracy","year":2019,"lang":"en","type":"article","venue":"Applied Health Economics and Health Policy","topic":"Economic and Financial Impacts of Cancer","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Stroke Network; Cancer Care Ontario; University of Toronto","funders":"","keywords":"Quality of Life Research; Health economics; Health administration; Public health; Health informatics; Medicine; Drug; Health services research; Oncology; Internal medicine; Environmental health; Pharmacology; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002554054,0.0008340346,0.001139551,0.001621626,0.0008196165,0.001921474,0.001384165,0.001065994,0.001933967],"category_scores_gemma":[0.009366658,0.0005183035,0.0008197465,0.002069008,0.0003172976,0.001028286,0.0007959002,0.0009815604,0.0003335194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003836609,"about_ca_system_score_gemma":0.004223811,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5764584,"about_ca_topic_score_gemma":0.5833721,"domain_scores_codex":[0.9990132,0.0003368722,0.00007825236,0.000205178,0.0002431363,0.0001233384],"domain_scores_gemma":[0.9964291,0.002014721,0.0001916112,0.0002222588,0.001040375,0.0001018707],"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":[0.0005940481,0.0001981066,0.03941399,0.000094111,0.0004399997,0.0001000765,0.0002012517,0.7782852,0.001623107,0.002105477,0.006680531,0.1702641],"study_design_scores_gemma":[0.00001639203,0.00001261536,0.002684177,0.000003507268,0.00002092015,0.000003531265,0.00002204758,0.9961447,0.0001905754,0.0005558867,0.0003387786,0.000006908509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7446746,0.001617996,0.2336405,0.003032336,0.0002248962,0.0002349605,0.005407371,0.001648907,0.009518453],"genre_scores_gemma":[0.9372767,0.0002210248,0.0574604,0.0001242391,0.0001020077,0.00005570864,0.002145929,0.00004631888,0.002567765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4235416,"threshold_uncertainty_score":0.8520719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07483135002436907,"score_gpt":0.3148517670346074,"score_spread":0.2400204170102383,"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."}}