{"id":"W2968147120","doi":"10.1097/tp.0000000000002923","title":"Seeing the Forest for the Trees: Random Forest Models for Predicting Survival in Kidney Transplant Recipients","year":2019,"lang":"en","type":"review","venue":"Transplantation","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Random forest; Machine learning; Computer science; Artificial intelligence; Decision tree; Recursive partitioning; Regression; Statistics; 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":[0.008235035,0.002122257,0.002314486,0.00189979,0.001002329,0.002269468,0.003278904,0.002679697,0.004796271],"category_scores_gemma":[0.01944145,0.0008969269,0.002257055,0.002636209,0.000789866,0.003524073,0.001747935,0.005595911,0.00208456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066961,"about_ca_system_score_gemma":0.001742865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01166598,"about_ca_topic_score_gemma":0.02171636,"domain_scores_codex":[0.9973431,0.00166872,0.0001138035,0.0003979805,0.0003581249,0.0001183455],"domain_scores_gemma":[0.9885626,0.009571357,0.0005173638,0.0004089422,0.0006667749,0.0002729396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006824501,0.000241367,0.01255764,0.0006831517,0.0006551336,0.0002688503,0.0002531162,0.5123218,0.0004480022,0.03060942,0.1201574,0.3211216],"study_design_scores_gemma":[0.00008077783,0.0001116588,0.0008843297,0.0002346447,0.00009951841,0.00007875422,0.00003622744,0.9098864,0.00018893,0.07463527,0.0136978,0.00006568575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0125531,0.01524046,0.9498734,0.01093841,0.001756913,0.0004169259,0.003522251,0.002523761,0.003174718],"genre_scores_gemma":[0.3094474,0.02306954,0.6361995,0.004793367,0.006060037,0.00168721,0.0091943,0.001459393,0.008089355],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01166598,"threshold_uncertainty_score":0.04355156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3192333719941502,"score_gpt":0.4885849999815572,"score_spread":0.169351627987407,"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."}}