{"id":"W2614092723","doi":"10.1016/j.annepidem.2017.05.002","title":"Ethics, big data and computing in epidemiology and public health","year":2017,"lang":"en","type":"article","venue":"Annals of Epidemiology","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McMaster University","funders":"","keywords":"Big data; Epidemiology; Medicine; Harm; Public health; Confidentiality; Research ethics; Engineering ethics; Information ethics; Public relations; Political science; Law; Computer science; Pathology; Data mining; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0668697,0.0004630531,0.001282574,0.002299287,0.003474772,0.01350896,0.001335207,0.008321679,0.003478979],"category_scores_gemma":[0.1800839,0.0006598089,0.0005142032,0.003458406,0.03381631,0.01368876,0.0050872,0.01338138,0.0005827175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005349534,"about_ca_system_score_gemma":0.01309156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004908435,"about_ca_topic_score_gemma":0.004420313,"domain_scores_codex":[0.928289,0.05962452,0.002563556,0.002083769,0.006426587,0.001012507],"domain_scores_gemma":[0.5842574,0.3759845,0.009473769,0.01425801,0.01010559,0.005920718],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004712407,0.00005625208,0.003152475,0.0004500726,0.00004917805,0.0001066294,0.001620103,0.0007548304,0.0001057495,0.9258901,0.03528466,0.03248274],"study_design_scores_gemma":[0.00002340545,0.00001513443,0.000917147,0.0004672122,0.00001164989,0.0001136879,0.0008486066,0.001423069,0.0001098541,0.9616346,0.03441384,0.00002173906],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.007786734,0.04190139,0.0665056,0.8466814,0.005496018,0.00008822331,0.0002977355,0.00008795298,0.03115488],"genre_scores_gemma":[0.6624898,0.04520322,0.05872736,0.1841086,0.03761225,0.0009461979,0.0002957043,0.0003626782,0.01025401],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9331303,"threshold_uncertainty_score":0.353645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6947970834722285,"score_gpt":0.5445765864036305,"score_spread":0.150220497068598,"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."}}