{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04639617,0.001346189,0.0007358554,0.004313031,0.00280142,0.003954887,0.00281498,0.002417793,0.01599357],"category_scores_gemma":[0.05700938,0.0009600798,0.001834799,0.002625656,0.003152648,0.004138839,0.01184053,0.003357628,0.002111443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006240174,"about_ca_system_score_gemma":0.01438604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002342408,"about_ca_topic_score_gemma":0.005167108,"domain_scores_codex":[0.9522485,0.03817342,0.001753018,0.001946877,0.004292426,0.001585779],"domain_scores_gemma":[0.9593715,0.02783591,0.002593577,0.002872512,0.004748449,0.002578065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001200531,0.001062425,0.007622163,0.004484613,0.000455961,0.002465466,0.06363683,0.03719111,0.0149654,0.1097596,0.03540676,0.7217491],"study_design_scores_gemma":[0.0007126447,0.002021155,0.0101054,0.003895893,0.0003214905,0.002324679,0.05026518,0.1364574,0.01295155,0.4838314,0.2966518,0.0004613729],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06769153,0.0006754993,0.8457326,0.02008507,0.0004082982,0.0161431,0.0007243194,0.0009336428,0.04760591],"genre_scores_gemma":[0.1303123,0.0003350225,0.8601063,0.001724539,0.00007783079,0.004717472,0.0002096669,0.00007933998,0.002437456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04639617,"threshold_uncertainty_score":0.2453694,"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."}}