{"id":"W4404035252","doi":"10.1101/2024.11.01.24316601","title":"A confounder debiasing method for RCT-like comparability enables Machine Learning-based personalization of survival benefit in living donor liver transplantation","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; University Health Network; Toronto General Hospital; University of Toronto; McGill University Health Centre","funders":"","keywords":"Debiasing; Comparability; Randomized controlled trial; Personalization; Confounding; Living donor liver transplantation; Medicine; Transplantation; Computer science; Liver transplantation; Surgery; Psychology; Internal medicine; World Wide Web; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1534082,0.0008248228,0.001915967,0.002733079,0.0006781249,0.001672386,0.001738283,0.001699276,0.005812774],"category_scores_gemma":[0.3724588,0.0005923028,0.003003194,0.001993214,0.002005677,0.002046767,0.003089503,0.002891911,0.0004331531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009541801,"about_ca_system_score_gemma":0.002472396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007721588,"about_ca_topic_score_gemma":0.0007453983,"domain_scores_codex":[0.8411953,0.1389446,0.007315243,0.008058978,0.003968263,0.0005176825],"domain_scores_gemma":[0.600869,0.3393593,0.01990192,0.03216312,0.006590597,0.001116043],"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":[0.006474989,0.0007446606,0.1185171,0.00220248,0.008746381,0.0005146216,0.001015628,0.1499349,0.005797748,0.04340205,0.007965448,0.6546841],"study_design_scores_gemma":[0.001488616,0.002142131,0.0198598,0.0003501117,0.001347208,0.0003395458,0.0001310921,0.8735,0.007836786,0.08385607,0.009020304,0.0001282552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04567029,0.0007113458,0.9487237,0.001128132,0.0002156376,0.00116543,0.0005262532,0.001015606,0.0008437095],"genre_scores_gemma":[0.6095403,0.0001409051,0.3864221,0.0006236184,0.0001997468,0.002045007,0.0004917544,0.0001458917,0.0003907391],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1534082,"threshold_uncertainty_score":0.8113101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04114919917189901,"score_gpt":0.3309808141394465,"score_spread":0.2898316149675474,"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."}}