{"id":"W3109322135","doi":"10.1093/jlb/lsaa083","title":"Transparency too little, too late? Why and how Health Canada should make clinical data and regulatory decision-making open to scrutiny in the face of COVID-19","year":2020,"lang":"en","type":"article","venue":"Journal of Law and the Biosciences","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Dalhousie University","funders":"Canadian Institutes of Health Research","keywords":"Scrutiny; Transparency (behavior); Coronavirus disease 2019 (COVID-19); Open data; Face (sociological concept); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Public relations; Business; Internet privacy; Political science; Medicine; Infectious disease (medical specialty); Computer science; Virology; Sociology; Disease; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006302266,0.00006892093,0.0003611563,0.00004764529,0.0001881256,0.0001673047,0.0008372157,0.0000284998,0.000003725106],"category_scores_gemma":[0.0006892863,0.00004098805,0.0000214072,0.0001968077,0.0002517816,0.000216305,0.0002516732,0.0001381288,1.064553e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002392488,"about_ca_system_score_gemma":0.0003174507,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09179879,"about_ca_topic_score_gemma":0.1799231,"domain_scores_codex":[0.9987737,0.0001260587,0.0006187188,0.0002227669,0.0001057164,0.0001530706],"domain_scores_gemma":[0.9986241,0.0005342499,0.0004188915,0.0001997809,0.00001404869,0.0002088896],"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":[0.0004992582,0.0000923611,0.05057688,0.0003051607,0.00006582665,0.00003078017,0.01583636,0.0001062955,0.00000135869,0.8487806,0.03445852,0.04924664],"study_design_scores_gemma":[0.001271002,0.0004380693,0.05433126,0.0001051458,0.000009480943,0.00001864599,0.00329678,0.001157748,9.631952e-7,0.02049797,0.9187325,0.0001404931],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2554674,0.006382964,0.001734939,0.7345633,0.0002507114,0.0004010959,0.0001983549,0.000001522044,0.0009997227],"genre_scores_gemma":[0.9234117,0.002042222,0.0003955476,0.07409287,0.0000432486,8.953406e-7,3.47512e-7,0.000002158775,0.00001096406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8842739,"threshold_uncertainty_score":0.914249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3147006229163092,"score_gpt":0.4130119418054523,"score_spread":0.09831131888914302,"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."}}