{"id":"W2963093468","doi":"10.1111/petr.13554","title":"Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data","year":2019,"lang":"en","type":"article","venue":"Pediatric Transplantation","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Medicine; Liver transplantation; Transplantation; Surgery; Internal medicine","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":["metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.006904283,0.0004125969,0.00044633,0.001079292,0.0002744714,0.000712112,0.0004031549,0.0002673581,0.0008190473],"category_scores_gemma":[0.01450013,0.000159266,0.0008870359,0.001378298,0.0003936489,0.0008956417,0.000668122,0.0007522606,0.0001664519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004070351,"about_ca_system_score_gemma":0.001001104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003030129,"about_ca_topic_score_gemma":0.002262484,"domain_scores_codex":[0.997427,0.001839531,0.0001102372,0.0002713921,0.0002087123,0.0001430852],"domain_scores_gemma":[0.9829373,0.01250003,0.002278745,0.0009146499,0.0009146699,0.00045467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.0002684174,0.00007402714,0.9934309,0.0000215599,0.00009921411,0.00009532333,0.0001896555,0.0006416984,0.0001398058,0.00006831429,0.00009046091,0.00488062],"study_design_scores_gemma":[0.00002978886,0.001356289,0.9823387,0.000037936,0.0001807516,0.0006166171,0.0009351847,0.01283877,0.0006906406,0.0002314952,0.0007288444,0.00001482626],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978076,0.0001306548,0.001403504,0.00003735232,0.000002090731,0.00002781478,0.0004060443,0.000008219406,0.0001767444],"genre_scores_gemma":[0.996811,0.00009334791,0.002447173,0.00001387121,0.000006386342,0.00004298197,0.0005362601,0.000008731759,0.00004032506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9995537,"threshold_uncertainty_score":0.03651381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1204019054463293,"score_gpt":0.3777948887319336,"score_spread":0.2573929832856043,"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."}}