{"id":"W2909577051","doi":"10.2196/11342","title":"Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings","year":2019,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Dashboard; Usability; Dissemination; Context (archaeology); Computer science; Population; Data science; Task (project management); Data collection; Knowledge management; Medicine; Engineering; Geography; Environmental health; Human–computer interaction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01059009,0.0002165978,0.0008475034,0.0004460123,0.0003247985,0.0002647264,0.001203501,0.000187142,0.0001099043],"category_scores_gemma":[0.005839829,0.0001919575,0.0001284303,0.001164081,0.00008572787,0.001108282,0.001432193,0.0009092752,0.0004085009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004900811,"about_ca_system_score_gemma":0.001120156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001096401,"about_ca_topic_score_gemma":0.00007854836,"domain_scores_codex":[0.994899,0.0007988124,0.001120744,0.0008309624,0.001349535,0.001000992],"domain_scores_gemma":[0.9941059,0.002895484,0.0002528317,0.001587178,0.0006371106,0.0005215363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.06088724,0.0005087589,0.725481,0.002101589,0.0002777371,0.000009449539,0.002170806,0.00001443994,0.00001114196,0.005708251,0.1366503,0.06617931],"study_design_scores_gemma":[0.02287565,0.00252821,0.9078893,0.001327649,0.000006614375,0.000005392612,0.0004949762,0.002342384,0.00001525861,0.001882941,0.06026335,0.0003682976],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9610152,0.00007769081,0.0194901,0.008198354,0.0009320113,0.009124647,0.0007845842,0.0001643684,0.0002130853],"genre_scores_gemma":[0.9753203,0.00007064744,0.01771751,0.001561489,0.0007808502,0.0004827435,0.003861688,0.00005888327,0.000145896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1824083,"threshold_uncertainty_score":0.7827799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3584710525886581,"score_gpt":0.599659220947585,"score_spread":0.2411881683589268,"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."}}