{"id":"W4399482208","doi":"10.1136/annrheumdis-2024-eular.1387","title":"POS1014 USE OF AN INTERACTIVE ADAPTIVE-TRIAL AI SIMULATION TOOL TO INCREASE EFFICIENCY AND PROMOTE INFORMED DECISION MAKING WITHIN LUPUS CLINICAL TRIAL TEAMS, A CASE STUDY","year":2024,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Systemic lupus erythematosus; Interactive simulation; Management science; Clinical trial; Group decision-making; Clinical decision making; Knowledge management; Machine learning; Medical physics; Artificial intelligence; Human–computer interaction; Psychology; Medicine; Simulation; Intensive care medicine; Engineering; Social psychology","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":[0.0004972977,0.0001459237,0.0002823338,0.0001506492,0.00004111476,0.00008046903,0.0001283913,0.00005641822,0.000009573065],"category_scores_gemma":[0.00300043,0.0000995618,0.0001027634,0.000302714,0.00004970682,0.0004167781,0.00007919926,0.000132317,0.000002077729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000237781,"about_ca_system_score_gemma":0.0001058482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001196034,"about_ca_topic_score_gemma":0.00002263173,"domain_scores_codex":[0.9985617,0.00009873208,0.0007401592,0.0001730605,0.0002943632,0.0001319834],"domain_scores_gemma":[0.9981822,0.001238141,0.00009023791,0.0002740326,0.0000802186,0.0001351302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.06788261,0.003219724,0.0003708665,0.001441973,0.0005667831,0.00006940294,0.01099022,0.2745248,0.00001275919,0.00007523654,0.0009636594,0.6398819],"study_design_scores_gemma":[0.01063027,0.002235153,0.002575218,0.001979008,0.0001594272,0.00002815984,0.0008117461,0.9809324,0.00001727031,0.00039161,0.00004779305,0.0001920151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924854,0.0001016216,0.004480422,0.00003495533,0.001498765,0.001271274,0.00003947565,0.0000854825,0.000002625322],"genre_scores_gemma":[0.9991775,0.00001061182,0.0006346792,0.00002689419,0.00008116418,0.0000406831,0.000004085798,0.00001813356,0.000006206974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7064075,"threshold_uncertainty_score":0.4060012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06516686963904823,"score_gpt":0.4010096852952256,"score_spread":0.3358428156561774,"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."}}