{"id":"W4386401250","doi":"10.1101/2023.08.31.23294918","title":"Benefit-Risk Assessment of Medical Products Using Bayesian Multi-Criteria Augmented Decision Analysis for Clinical Development","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Bayesian probability; Risk analysis (engineering); Computer science; Business; Artificial intelligence","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":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04785207,0.0006563977,0.003736355,0.0006194297,0.0001633258,0.00009478207,0.001535627,0.001433578,0.0003828963],"category_scores_gemma":[0.3514784,0.0005553744,0.001151657,0.00103386,0.0003055974,0.00003881056,0.002353664,0.001493303,0.00000738093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002317819,"about_ca_system_score_gemma":0.00119076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004130952,"about_ca_topic_score_gemma":0.00009911626,"domain_scores_codex":[0.9855302,0.002424672,0.006718898,0.001976057,0.002688294,0.0006618536],"domain_scores_gemma":[0.9109748,0.08236668,0.002910422,0.002074088,0.001126879,0.000547111],"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":[0.001036139,0.007998721,0.7882177,0.008203235,0.02742036,0.0001566644,0.0007793642,0.001276475,0.0002046511,0.007745011,0.002308116,0.1546536],"study_design_scores_gemma":[0.003516492,0.0001872027,0.2311001,0.002184214,0.006824986,0.00000165747,0.00005600764,0.3785262,0.0006005199,0.375787,0.0002677731,0.0009478811],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2645232,0.00003325779,0.7295113,0.0002888652,0.003337505,0.001711401,0.0004484435,0.000138437,0.000007570263],"genre_scores_gemma":[0.04617156,0.000324883,0.9522538,0.00004267845,0.0006300954,0.0002830561,0.0000734686,0.0001469205,0.00007359451],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5571176,"threshold_uncertainty_score":0.9998628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7750856687358957,"score_gpt":0.6690172628057338,"score_spread":0.1060684059301619,"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."}}