{"id":"W3016378832","doi":"10.1177/0272989x20912402","title":"Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies","year":2020,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Center for Advancing Translational Sciences; NIH Clinical Center; National Cancer Institute; Medical Research Council; National Institute for Health and Care Research; Canadian Institutes of Health Research; Norges Forskningsråd; National Institute of Allergy and Infectious Diseases; Stanford University","keywords":"Monte Carlo method; Sample size determination; Computer science; Range (aeronautics); Sample (material); Computation; Observational study; Mathematical optimization; Econometrics; Statistics; Data mining; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.08408643,0.00130156,0.001859545,0.002772369,0.001432125,0.0040457,0.004466147,0.00450071,0.003766056],"category_scores_gemma":[0.2761306,0.001054798,0.003516638,0.003820277,0.002601798,0.003825283,0.002902217,0.004300023,0.0002930136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005527398,"about_ca_system_score_gemma":0.004317155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009459039,"about_ca_topic_score_gemma":0.01107253,"domain_scores_codex":[0.922996,0.06721736,0.002934295,0.00135341,0.004715974,0.0007829361],"domain_scores_gemma":[0.4839354,0.4968067,0.006886811,0.005981674,0.005440739,0.0009487915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001163844,0.0006281524,0.0262365,0.003072651,0.0008608356,0.004204514,0.001632555,0.6437399,0.0006069079,0.1746324,0.005819064,0.1374027],"study_design_scores_gemma":[0.0004388934,0.0007948481,0.004621707,0.002203146,0.0005872892,0.002507207,0.0009136526,0.7964763,0.001992414,0.1763345,0.01296031,0.0001697448],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063119,0.009749078,0.8571527,0.008871245,0.0002282507,0.001588145,0.0005961506,0.0001830045,0.01531953],"genre_scores_gemma":[0.444857,0.004613136,0.5462809,0.0007082001,0.000149194,0.002086059,0.0002732961,0.0000775859,0.0009546211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08408643,"threshold_uncertainty_score":0.4446969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4239202989280365,"score_gpt":0.5099003153606245,"score_spread":0.08598001643258807,"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."}}