{"id":"W2249217595","doi":"","title":"Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study","year":2014,"lang":"en","type":"review","venue":"RECERCAT (Consorci de Serveis Universitaris de Catalunya)","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institució Catalana de Recerca i Estudis Avançats; Royal Bank of Canada","keywords":"Personalized medicine; Variety (cybernetics); Population; Psychological intervention; Set (abstract data type); Computer science; Actuarial science; Treatment and control groups; Outcome (game theory); Machine learning; Medicine; Artificial intelligence; Economics; Microeconomics; Bioinformatics; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01811099,0.001888355,0.005017097,0.00383343,0.0004942088,0.002930939,0.003282893,0.003517915,0.004144145],"category_scores_gemma":[0.03973208,0.00114043,0.003436622,0.003937813,0.003321173,0.003296888,0.001179564,0.004424543,0.0008129416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003530496,"about_ca_system_score_gemma":0.004426705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00380006,"about_ca_topic_score_gemma":0.003156971,"domain_scores_codex":[0.9886679,0.007261724,0.001253575,0.0009235592,0.001731192,0.0001620213],"domain_scores_gemma":[0.9604685,0.03656233,0.00118454,0.0005969297,0.001050679,0.0001370874],"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":[0.0003715426,0.0001748703,0.0004273257,0.04866859,0.0009936232,0.0001025693,0.0001662213,0.008283812,0.000130386,0.1243461,0.01906985,0.7972651],"study_design_scores_gemma":[0.00113684,0.0006633022,0.002757858,0.1065209,0.003444652,0.001240755,0.0002578059,0.0236483,0.0007766364,0.4743201,0.3849493,0.0002835119],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001839093,0.9850139,0.01102772,0.002416606,0.0002613764,0.00007500131,0.00004594267,0.00002038944,0.0009551612],"genre_scores_gemma":[0.01473602,0.9482358,0.03224416,0.002220699,0.00120447,0.0006004375,0.000107158,0.00004078124,0.0006104889],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01811099,"threshold_uncertainty_score":0.09578121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5773127238043168,"score_gpt":0.6079543282309487,"score_spread":0.030641604426632,"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."}}