{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01572176,0.0008627468,0.005448767,0.0004174203,0.0002313274,0.00007402385,0.0006869799,0.0005603217,0.0002248105],"category_scores_gemma":[0.04177679,0.0007479709,0.001785481,0.0005468726,0.0003074878,0.0001816746,0.0003020315,0.0005262125,0.000004699906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008811113,"about_ca_system_score_gemma":0.00125857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005672739,"about_ca_topic_score_gemma":0.0000974115,"domain_scores_codex":[0.9829686,0.01266223,0.00234317,0.001158172,0.0002601471,0.0006077338],"domain_scores_gemma":[0.9484583,0.04733558,0.002005978,0.001227142,0.0003861178,0.000586844],"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":[0.0002038528,0.0008331966,0.0000361463,0.2165922,0.001597974,0.0002452244,0.0004036346,2.642822e-7,3.291887e-7,0.002533334,0.0007537506,0.7768001],"study_design_scores_gemma":[0.009157605,0.0058143,0.00002008045,0.37436,0.05000056,0.004686013,0.006147183,0.0002400025,0.000008630933,0.02050766,0.5263656,0.002692305],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004675785,0.9353502,0.05531745,0.00008029945,0.0001501559,0.007265151,0.001164514,0.0001046276,0.00009997653],"genre_scores_gemma":[0.000001662147,0.5141937,0.4851121,0.00001422092,0.00009158719,0.0001538929,0.00005141611,0.00007989749,0.000301504],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7741078,"threshold_uncertainty_score":0.9994971,"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."}}