{"id":"W3046931506","doi":"10.1007/s11845-020-02332-1","title":"Machine learning for predicting long-term kidney allograft survival: a scoping review","year":2020,"lang":"en","type":"review","venue":"Irish Journal of Medical Science (1971 -)","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; University Health Network; Toronto General Hospital; University of Toronto; McMaster University; Impact","funders":"","keywords":"Medicine; Term (time); Intensive care medicine; Kidney transplantation; Machine learning; Artificial intelligence; Kidney; 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":[],"consensus_categories":[],"category_scores_codex":[0.006752082,0.001561956,0.006740005,0.005772149,0.0003594898,0.002952426,0.002120671,0.002487461,0.003401867],"category_scores_gemma":[0.01695009,0.0007789662,0.006095424,0.005558585,0.0008276543,0.002576678,0.001267916,0.002347381,0.0005414635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001838496,"about_ca_system_score_gemma":0.004183573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006095566,"about_ca_topic_score_gemma":0.01044601,"domain_scores_codex":[0.9978271,0.0007011908,0.0006286476,0.0003286919,0.0004356349,0.00007871907],"domain_scores_gemma":[0.9844953,0.01346667,0.001162057,0.0001460358,0.0006408276,0.00008910312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000286058,0.00009288891,0.001102836,0.402643,0.008642637,0.00009685254,0.0001193023,0.001158286,0.0002474495,0.001809067,0.005245768,0.5785559],"study_design_scores_gemma":[0.0003090025,0.0005329563,0.007755575,0.8028147,0.0643799,0.000795294,0.0003417369,0.001890004,0.0006286924,0.004172317,0.1162418,0.0001380794],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006438069,0.9996835,0.00006961033,0.00006571745,0.0000305116,0.00000764963,0.00002175317,0.000001748434,0.00005513636],"genre_scores_gemma":[0.001368827,0.9981647,0.0002557227,0.00008729174,0.00004375129,0.00001638303,0.00002901314,0.000001301301,0.0000328984],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006752082,"threshold_uncertainty_score":0.0357089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1639188942290614,"score_gpt":0.4727658404781213,"score_spread":0.3088469462490598,"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."}}