{"id":"W4388824558","doi":"10.1016/j.jcpo.2023.100441","title":"A tailored approach to horizon scanning for cancer medicines","year":2023,"lang":"en","type":"article","venue":"Journal of Cancer Policy","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre","funders":"Genentech; Eisai; National Institutes of Health; Regeneron Pharmaceuticals; Natera; Italfarmaco; Seagen; Array BioPharma; Ipsen; BeiGene; Pfizer; Innovent Biologics; Les Laboratories Pierre Fabre; Sun Pharma; Daiichi Sankyo Europe; National Cancer Institute; Gilead Sciences; Servier; Memorial Sloan-Kettering Cancer Center; Bristol-Myers Squibb; Eli Lilly and Company; AstraZeneca; American Society of Clinical Oncology; Qbiotics; Hexal AG; Sanofi; Amgen","keywords":"Medicine; Context (archaeology); Delphi method; Reimbursement; Analytic hierarchy process; Quality of life (healthcare); Family medicine; Health care; Operations research; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007671127,0.000163936,0.001009164,0.001221741,0.0001426679,0.00005955681,0.0003487774,0.00009827562,0.00006448016],"category_scores_gemma":[0.003061684,0.0001750134,0.0001979431,0.0007960615,0.00003884824,0.0003246929,0.0000376203,0.0001590586,0.0001146574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049669,"about_ca_system_score_gemma":0.0008596638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003476243,"about_ca_topic_score_gemma":0.0002118848,"domain_scores_codex":[0.9960271,0.00007483792,0.002994856,0.0002733278,0.0001452416,0.0004846042],"domain_scores_gemma":[0.9964064,0.0003777578,0.002426393,0.000221765,0.0002260411,0.0003416807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001701723,0.00008714405,0.02435163,0.001015355,0.0004521808,0.000001762591,0.009927068,0.01573442,0.00009001566,0.0852327,0.8545716,0.008366002],"study_design_scores_gemma":[0.002725769,0.0004958077,0.03586827,0.0005292304,0.00004308339,0.00001404724,0.002474901,0.00860945,0.00005952048,0.01769211,0.9309761,0.0005117353],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3097705,0.01348362,0.01671818,0.6452704,0.004655144,0.001802339,0.0007773446,0.00009924403,0.007423244],"genre_scores_gemma":[0.890935,0.003578876,0.009348933,0.05905317,0.03010661,0.001276081,0.00002503277,0.0001929765,0.005483344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5862172,"threshold_uncertainty_score":0.7136838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3954448321438268,"score_gpt":0.5134311021885223,"score_spread":0.1179862700446955,"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."}}