{"id":"W4412805152","doi":"10.1016/j.ijmedinf.2025.106045","title":"Privacy-by-design: Case studies in interactive record linkage using a hybrid human-computer system","year":2025,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services","funders":"National Center for Advancing Translational Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health; Patient-Centered Outcomes Research Institute","keywords":"Computer science; Record linkage; Linkage (software); Human–computer interaction; Gene; Medicine; Genetics; Biology","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":[0.00949777,0.0001279352,0.0004239065,0.0008549123,0.00006813047,0.0003457624,0.001856455,0.00006616079,0.00008306102],"category_scores_gemma":[0.003747519,0.00009005195,0.0001199979,0.0002883934,0.0001322556,0.001166245,0.0009228471,0.0004745502,0.00002542396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004297066,"about_ca_system_score_gemma":0.000193624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004135791,"about_ca_topic_score_gemma":0.00001154664,"domain_scores_codex":[0.9938621,0.0003218091,0.002619422,0.0001045253,0.002937065,0.0001551048],"domain_scores_gemma":[0.9955336,0.002006812,0.001163822,0.0002195374,0.0009617823,0.000114425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003577777,0.0006720017,0.0005328418,0.0003725413,0.002052282,0.02154459,0.02085625,0.002698265,0.00002329868,0.01418382,0.5128233,0.423883],"study_design_scores_gemma":[0.005742697,0.0004168701,0.00006635352,0.007496524,0.0001260429,0.0135569,0.08459061,0.7598925,0.0004724531,0.01796479,0.1091975,0.0004768436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1746017,0.000102336,0.8182509,0.002053753,0.004266173,0.0001355761,0.00002400039,0.00001100339,0.000554597],"genre_scores_gemma":[0.9703865,0.0001221501,0.02627157,0.002636965,0.0003808581,0.000003429732,0.000005558538,0.000005752385,0.0001872057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7957848,"threshold_uncertainty_score":0.44864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2472529296664974,"score_gpt":0.5161682721501035,"score_spread":0.2689153424836062,"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."}}