{"id":"W4386878128","doi":"10.1097/tp.0000000000004783","title":"Using Regression Equations to Enhance Interpretation of Histology Lesions of Kidney Transplant Rejection","year":2023,"lang":"en","type":"article","venue":"Transplantation","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Metabolomics Innovation Centre; University of Alberta","funders":"Ministry of Advanced Education; University of Alberta; Natera; Ministry of Advanced Education and Technology; Mendez National Institute of Transplantation Foundation; Genome Canada","keywords":"Histology; Interpretation (philosophy); Kidney transplant; Regression; Medicine; Kidney; Kidney transplantation; Pathology; Statistics; Mathematics; Internal medicine; Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02022779,0.002074184,0.001105192,0.002120415,0.0003554486,0.001262859,0.001151535,0.0009449513,0.003133696],"category_scores_gemma":[0.05205013,0.0006744012,0.00262052,0.00101213,0.0004361645,0.001068505,0.001055037,0.002402847,0.00123421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00124116,"about_ca_system_score_gemma":0.001618531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0109532,"about_ca_topic_score_gemma":0.01161611,"domain_scores_codex":[0.9921781,0.005614621,0.0004638766,0.00103738,0.0004500503,0.0002559006],"domain_scores_gemma":[0.9436772,0.04728315,0.004332107,0.001095482,0.003310031,0.0003020881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001176644,0.0006856133,0.4966851,0.000426333,0.002043693,0.0003412021,0.0003888409,0.3241623,0.003120674,0.002635743,0.007881602,0.1604523],"study_design_scores_gemma":[0.0001096365,0.0002950742,0.03122531,0.000095709,0.0002972448,0.0001672858,0.00004767518,0.9612028,0.001512646,0.003224781,0.001763759,0.00005816582],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4444299,0.001512986,0.5422992,0.002615879,0.0002930038,0.0005116724,0.002879544,0.002669823,0.002788015],"genre_scores_gemma":[0.8676732,0.0002903882,0.1277755,0.0004319383,0.0001261366,0.000242918,0.002045919,0.0002172345,0.001196839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02022779,"threshold_uncertainty_score":0.106976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05363865119925208,"score_gpt":0.380816775032514,"score_spread":0.3271781238332619,"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."}}