{"id":"W4316814710","doi":"10.18280/ria.360612","title":"Assessment of Successful Randomization Through a Machine Learning and Visualization Tool for Pre-Treatment Symptoms: Examples from CCTG/AGITG CO.17 and CO.20 Trials","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; Queen's University; Princess Margaret Cancer Centre; Ottawa Hospital; University of Ottawa; University of Toronto","funders":"Princess Margaret Cancer Foundation","keywords":"Randomization; Randomized controlled trial; Baseline (sea); Medicine; Artificial intelligence; Machine learning; Computer science; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2090291,0.00234667,0.002777508,0.008158,0.001295356,0.00500582,0.001843136,0.003011799,0.008220066],"category_scores_gemma":[0.4955053,0.001170245,0.005384905,0.007123064,0.001927802,0.003764464,0.003361318,0.003224937,0.001202638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002762279,"about_ca_system_score_gemma":0.004753424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002409225,"about_ca_topic_score_gemma":0.003754377,"domain_scores_codex":[0.7172226,0.2406977,0.02187664,0.003167449,0.01608115,0.0009545571],"domain_scores_gemma":[0.3032931,0.6201855,0.02894477,0.02319608,0.02341295,0.0009676435],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008902744,0.0005037994,0.01649444,0.05525534,0.006703412,0.0009075855,0.010438,0.0131763,0.00377115,0.03375556,0.1052144,0.7448773],"study_design_scores_gemma":[0.02507287,0.008336112,0.06513152,0.09421748,0.01712549,0.003140287,0.00439208,0.1227302,0.03095084,0.1827164,0.4440034,0.002183302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1038063,0.07497697,0.6791347,0.04459105,0.003634223,0.0304698,0.02002952,0.01794663,0.02541078],"genre_scores_gemma":[0.2548786,0.007307286,0.7073488,0.003412503,0.0004514507,0.02089653,0.002493754,0.00154093,0.001670218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7909709,"threshold_uncertainty_score":0.9754079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4762313252496092,"score_gpt":0.528125142559388,"score_spread":0.05189381730977882,"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."}}