{"id":"W2214448182","doi":"10.1053/j.ajkd.2015.11.007","title":"Predicting Progression in CKD: Perspectives and Precautions","year":2015,"lang":"en","type":"review","venue":"American Journal of Kidney Diseases","topic":"Chronic Kidney Disease and Diabetes","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Providence Health Care; University of British Columbia","funders":"","keywords":"Predictive modelling; Medicine; Kidney disease; USable; Intensive care medicine; Clinical Practice; Resource (disambiguation); Disease; Risk analysis (engineering); Management science; Computer science; Machine learning; Pathology; Internal medicine; Physical therapy; Engineering","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":[0.005020476,0.001567398,0.005235607,0.002967355,0.0004580744,0.003062638,0.00252796,0.002764279,0.002139106],"category_scores_gemma":[0.008259988,0.0006488697,0.002387448,0.002768845,0.0009797268,0.003221382,0.001307917,0.005791743,0.000821477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206371,"about_ca_system_score_gemma":0.003365614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004811842,"about_ca_topic_score_gemma":0.009647287,"domain_scores_codex":[0.9983352,0.0004371311,0.000370936,0.0002597805,0.0005112462,0.0000856786],"domain_scores_gemma":[0.9933692,0.004105371,0.0006596091,0.00009436366,0.001568474,0.0002030578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001635721,0.0001367675,0.002692808,0.02468851,0.0007675018,0.0001257954,0.00009420975,0.0005031688,0.0001780757,0.003298905,0.02820253,0.9391482],"study_design_scores_gemma":[0.0002025172,0.0005600815,0.01379987,0.08903018,0.00580444,0.002514311,0.0007059588,0.00168618,0.0007011039,0.01603056,0.8687228,0.0002419772],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005426543,0.9987264,0.00009635049,0.0007596855,0.000203981,0.000002513078,0.00001704875,0.000003892201,0.0001357498],"genre_scores_gemma":[0.001079235,0.9970928,0.0004801927,0.0006287787,0.0005855197,0.000006170255,0.00002870718,0.00000148781,0.00009713105],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005235607,"threshold_uncertainty_score":0.02655107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02316480606887705,"score_gpt":0.3749622156809869,"score_spread":0.3517974096121098,"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."}}