{"id":"W2948576179","doi":"10.1109/cybermatics_2018.2018.00275","title":"Building Trust & Protecting Privacy: Analyzing Evidentiary Quality in a Blockchain Proof-of-Concept for Health Research Data Consent Management","year":2018,"lang":"en","type":"article","venue":"","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure; University of British Columbia","funders":"Genome Canada","keywords":"Blockchain; Trustworthiness; Computer science; Computer security; Data quality; Proof of concept; Information privacy; Data management; Quality (philosophy); Health records; Process (computing); Medical record; Health data; Internet privacy; Business; Database; Health care; Medicine; Law; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01385328,0.0001419511,0.0003124316,0.0005490552,0.0005485332,0.00008388957,0.002972171,0.000106159,0.000007581183],"category_scores_gemma":[0.0008498341,0.0001379401,0.00004433768,0.001694757,0.0003469156,0.0001523186,0.002958367,0.0004255702,0.000002538662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146665,"about_ca_system_score_gemma":0.0002276437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007649758,"about_ca_topic_score_gemma":0.001358261,"domain_scores_codex":[0.9966336,0.0005351741,0.0007800176,0.001005902,0.000389535,0.0006558059],"domain_scores_gemma":[0.9964568,0.0006576597,0.0002744689,0.002274301,0.0002489024,0.00008785601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003599177,0.0005618125,0.001996269,0.0006329292,0.00008712482,0.000002218147,0.003470438,0.00004868145,0.000734125,0.5997378,0.002880129,0.3898124],"study_design_scores_gemma":[0.003308302,0.000822369,0.00430434,0.001306278,0.00002554964,0.00002865796,0.00228647,0.5697278,0.06405833,0.3181416,0.03478966,0.00120064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1131032,0.000273925,0.8744581,0.009349425,0.00004724998,0.002505979,0.000009460523,0.000147367,0.000105285],"genre_scores_gemma":[0.6126514,0.00000542703,0.3868712,0.00009563001,0.00003668333,0.0002647762,0.000003593542,0.000007174383,0.00006411959],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5696791,"threshold_uncertainty_score":0.5625036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.244323929510009,"score_gpt":0.46424815331903,"score_spread":0.219924223809021,"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."}}