{"id":"W2782740490","doi":"10.3390/diseases6010006","title":"Merging Digital Medicine and Economics: Two Moving Averages Unlock Biosignals for Better Health","year":2018,"lang":"en","type":"article","venue":"Diseases","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Digital health; Data science; Computer science; Economics; Health care; Economic growth","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":[0.00007476817,0.00010866,0.0002841265,0.0001096941,0.0001425602,0.00003635334,0.0000381673,0.00001675647,0.00003975126],"category_scores_gemma":[0.000128106,0.00008569907,0.00007353623,0.00006066592,0.000113304,0.00009755158,0.00002390388,0.00003296573,0.000006801141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003131697,"about_ca_system_score_gemma":0.00004249096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005796636,"about_ca_topic_score_gemma":0.000003110253,"domain_scores_codex":[0.9993089,0.00001009579,0.0001898943,0.0002257592,0.00006527356,0.0002000956],"domain_scores_gemma":[0.9993763,0.0001180039,0.0000710069,0.0001465533,0.00005392491,0.0002342266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002116318,0.0001712032,0.710646,0.0004914656,0.0005808586,0.00001311835,0.001103574,0.00001345189,0.0009243285,0.0002472293,0.01370098,0.2718961],"study_design_scores_gemma":[0.04561087,0.0127187,0.4884769,0.01108661,0.007581369,0.0001887555,0.01641506,0.07450265,0.01968518,0.02346515,0.2958159,0.004452844],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857323,0.002875726,0.0008954324,0.00964838,0.0001332625,0.00016667,0.00008170282,0.00007028211,0.0003962496],"genre_scores_gemma":[0.9955541,0.0001628745,0.0003240609,0.001683095,0.001906099,0.00001109474,0.0000667146,0.00001759946,0.0002743238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.282115,"threshold_uncertainty_score":0.3494706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02578538882347557,"score_gpt":0.3321864795913742,"score_spread":0.3064010907678986,"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."}}