{"id":"W4402454018","doi":"10.1101/2024.09.10.24313417","title":"External Control Arm with Synthetic Real-world Data for Comparative Oncology using Single Trial Arm Evidence (ECLIPSE): A Case Study using Lung-MAP S1400I","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Public Health Ontario; University of Toronto; Health Canada","funders":"","keywords":"Eclipse; Real world data; Real world evidence; Medicine; Control (management); Oncology; Computer science; Internal medicine; Artificial intelligence; Data science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.2226721,0.0008643813,0.002439059,0.00231237,0.00110154,0.003995429,0.002805694,0.004620689,0.01324008],"category_scores_gemma":[0.4488065,0.0008805215,0.0076722,0.003947283,0.001950546,0.002570959,0.003237777,0.00303309,0.001080387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00382412,"about_ca_system_score_gemma":0.005482617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002420837,"about_ca_topic_score_gemma":0.003045933,"domain_scores_codex":[0.6968683,0.2684562,0.01791101,0.006569919,0.008806593,0.001388015],"domain_scores_gemma":[0.3418477,0.5789111,0.0352323,0.03132582,0.01114688,0.001536294],"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":[0.1074646,0.00410047,0.1907545,0.1033195,0.06780356,0.009152221,0.005441967,0.04718614,0.002663143,0.1116912,0.06368763,0.2867351],"study_design_scores_gemma":[0.05120033,0.02919703,0.1214916,0.09514033,0.06411178,0.01274794,0.004946367,0.1053931,0.008325505,0.1275726,0.3787032,0.001170261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.470506,0.06077475,0.2807057,0.02715783,0.002501034,0.04042788,0.06367002,0.001033305,0.05322345],"genre_scores_gemma":[0.8549215,0.002553272,0.1070318,0.005112301,0.0003127888,0.0184115,0.01010749,0.0001711313,0.001378293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7773279,"threshold_uncertainty_score":0.9585837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2076818188546551,"score_gpt":0.4638796729267283,"score_spread":0.2561978540720732,"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."}}