{"id":"W2129397798","doi":"10.1093/bioinformatics/btv279","title":"Data Safe Havens in health research and healthcare","year":2015,"lang":"en","type":"review","venue":"Bioinformatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Ottawa; University of Toronto","funders":"Economic and Social Research Council; Medical Research Council; Canadian Institutes of Health Research; National Cancer Institute; Cancer Research UK; Wellcome Trust","keywords":"Context (archaeology); Health care; Interpretation (philosophy); Health informatics; Corporate governance; Data governance; Computer science; Biomedicine; Public relations; Data science; Work (physics); Trustworthiness; Knowledge management; Internet privacy; Political science; Business; Data quality; Law; Engineering; Service (business)","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":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.03402206,0.0005174064,0.0033503,0.001083369,0.0008813257,0.00003885668,0.001453502,0.001222676,0.0000376694],"category_scores_gemma":[0.001417366,0.0004150525,0.00006308269,0.001360546,0.0001508668,0.0003578787,0.001702316,0.006068695,0.001537021],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.00440413,"about_ca_system_score_gemma":0.03508759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00381552,"about_ca_topic_score_gemma":0.01022506,"domain_scores_codex":[0.9848327,0.005237528,0.005335213,0.000606859,0.001160217,0.002827498],"domain_scores_gemma":[0.9913362,0.002429029,0.001834233,0.00277036,0.0004917639,0.001138447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004000884,0.00002128808,0.00003900184,0.2639764,0.00001389719,0.000003035411,0.001823531,1.861316e-8,1.525062e-10,0.000929663,0.09725432,0.6359348],"study_design_scores_gemma":[0.0003569438,0.000274807,0.000004124059,0.06531613,0.00001203046,0.00002865114,0.002399911,0.0003736514,2.851363e-10,0.0001650639,0.93081,0.0002587303],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[7.118607e-7,0.9823826,0.00003049764,0.002110579,0.0009998541,0.007810191,0.00153623,0.00008802949,0.005041284],"genre_scores_gemma":[5.323753e-7,0.9912729,0.002289501,0.0005815547,0.0005990405,0.0004386754,0.00217791,0.0001024474,0.002537497],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8335556,"threshold_uncertainty_score":0.9998301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7803968123818096,"score_gpt":0.6547543979116747,"score_spread":0.1256424144701349,"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."}}