{"id":"W2728522947","doi":"10.1097/ede.0000000000000711","title":"Big Data and Population Health","year":2017,"lang":"en","type":"article","venue":"Epidemiology","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto","funders":"","keywords":"Big data; Data science; Population health; Poverty; Socioeconomic status; Population; Macro; Social determinants of health; Public health; Political science; Environmental health; Computer science; Medicine; Data mining","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":[0.03648562,0.0009248278,0.002162023,0.006715205,0.002506944,0.008453606,0.002219128,0.005628725,0.01023948],"category_scores_gemma":[0.09572043,0.0006661078,0.001347496,0.01221122,0.01044662,0.01231979,0.008205133,0.008888978,0.001289536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004808749,"about_ca_system_score_gemma":0.007297719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006971718,"about_ca_topic_score_gemma":0.005172364,"domain_scores_codex":[0.9719189,0.02066977,0.001286515,0.002362261,0.003164094,0.0005984629],"domain_scores_gemma":[0.8853555,0.09275322,0.005865075,0.00870871,0.004543982,0.002773422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001443236,0.00005875629,0.01430551,0.002446021,0.000629701,0.0002072002,0.002351794,0.003210282,0.000107843,0.6581455,0.1329005,0.1854926],"study_design_scores_gemma":[0.00002781548,0.00003379072,0.004782709,0.001801919,0.00006325743,0.0001272738,0.001140577,0.002167633,0.0000785388,0.8490986,0.1406226,0.00005522761],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.007515346,0.1937476,0.08049949,0.6330367,0.01324106,0.0003907031,0.01031668,0.0008107286,0.06044166],"genre_scores_gemma":[0.4072272,0.2845361,0.1224009,0.1263133,0.03665049,0.002508966,0.01041138,0.0005678069,0.009383854],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03648562,"threshold_uncertainty_score":0.1929567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4423928385943427,"score_gpt":0.5321099536020673,"score_spread":0.08971711500772456,"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."}}