{"id":"W4200059501","doi":"10.34067/kid.0005102021","title":"Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification","year":2021,"lang":"en","type":"article","venue":"Kidney360","topic":"AI in cancer detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Toronto General Hospital; University of Toronto; University Health Network; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research; Alport Syndrome Foundation; Faculty of Arts, Ryerson University","keywords":"Biomarker; Renal pathology; Glomerular basement membrane; Tuft; Pathology; Artificial intelligence; Medicine; Pattern recognition (psychology); Computer science; Internal medicine; Glomerulonephritis; Kidney; Biology; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009830444,0.0001016016,0.00009137279,0.00006573756,0.0001790957,0.0001398767,0.0001691132,0.00004759014,0.000009444],"category_scores_gemma":[0.0002108564,0.0001082815,0.00004249281,0.0003639804,0.00003402443,0.0004164683,0.0001029381,0.00004078323,0.00001068278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064849,"about_ca_system_score_gemma":0.0002358867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001047588,"about_ca_topic_score_gemma":0.000002354777,"domain_scores_codex":[0.9989843,0.00004648845,0.0001591213,0.0004254697,0.0001725354,0.0002120869],"domain_scores_gemma":[0.9990528,0.00005487855,0.00007450878,0.000432227,0.0001589099,0.0002266486],"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":[0.0002207683,0.0004278567,0.004312359,0.0006555159,0.0002787373,0.0001301977,0.002263241,0.0001965132,0.07240077,0.0390795,0.2369446,0.64309],"study_design_scores_gemma":[0.0009981238,0.0001083514,0.02807833,0.00006425432,0.00003249002,0.00002131503,0.0002381413,0.8067406,0.019598,0.00519641,0.1385315,0.0003925304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04102457,0.001382445,0.9406331,0.01234573,0.001626887,0.0007236781,0.0001206793,0.001333966,0.0008089078],"genre_scores_gemma":[0.9132324,0.00005790017,0.08486357,0.001125644,0.00006016549,0.000248546,0.00005115256,0.00002178261,0.0003388339],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8722078,"threshold_uncertainty_score":0.4415592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03143347048122483,"score_gpt":0.2647824826354243,"score_spread":0.2333490121541994,"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."}}