{"id":"W4229000427","doi":"10.2196/32456","title":"Exploring Human-Data Interaction in Clinical Decision-making Using Scenarios: Co-design Study","year":2022,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Connected Health Cities","keywords":"Pulmonary disease; Work (physics); Health professionals; Health care; Decision support system; Space (punctuation); Knowledge management; Clinical decision making; Medicine; Computer science; Engineering; Data mining; Intensive care medicine","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":[0.00176767,0.0001742639,0.0002708288,0.0005456828,0.0007249085,0.0004273662,0.002127959,0.00002970596,0.0001770956],"category_scores_gemma":[0.0001678118,0.000179902,0.00006109769,0.0007452554,0.00002894747,0.002070771,0.002061748,0.0004618808,0.00001356384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230527,"about_ca_system_score_gemma":0.0000655185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005820647,"about_ca_topic_score_gemma":0.00005984695,"domain_scores_codex":[0.9970018,0.0005561518,0.0007997679,0.0007264461,0.0006503178,0.0002655396],"domain_scores_gemma":[0.9979517,0.0004023318,0.0002822552,0.001244986,0.00004148596,0.00007724048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008554837,0.008898658,0.8498785,0.00004014744,0.0002048627,0.0004562796,0.0369105,0.03367693,0.0005913725,0.006594926,0.00706155,0.05560072],"study_design_scores_gemma":[0.002689326,0.0014946,0.365876,0.0002797309,0.00004732731,0.00001602889,0.02514455,0.5922601,0.00005464879,0.001341757,0.009413141,0.001382759],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8885637,0.000003991218,0.1103196,0.00000843189,0.0005339728,0.0003742433,0.00001117918,0.0001288569,0.00005607364],"genre_scores_gemma":[0.9982777,0.000001570119,0.001446786,0.00008316196,0.00007446375,0.00002250644,0.00005509558,0.00001949218,0.00001926386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5585832,"threshold_uncertainty_score":0.7336189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5343941342951667,"score_gpt":0.5162282768176758,"score_spread":0.01816585747749089,"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."}}