{"id":"W4389887102","doi":"10.1097/txd.0000000000001565","title":"Canadian Kidney Transplant Professionals’ Perspectives on Precision Medicine and Molecular Matching in Kidney Allocation","year":2023,"lang":"en","type":"article","venue":"Transplantation Direct","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health; University of British Columbia; Translational Research in Oncology; Université de Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Genome Alberta; Canadian Institutes of Health Research; Genome British Columbia; Genome Canada","keywords":"Medicine; Kidney transplant; Matching (statistics); Precision medicine; Kidney; Kidney transplantation; Intensive care medicine; Family medicine; Internal medicine; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.02031314,0.0003271886,0.0004502589,0.001349847,0.03126887,0.007723104,0.001392003,0.004377997,0.00503365],"category_scores_gemma":[0.02691475,0.000346164,0.0005769042,0.002623365,0.01453139,0.002168452,0.005180524,0.005433201,0.0002440317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08479228,"about_ca_system_score_gemma":0.19265,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.882722,"about_ca_topic_score_gemma":0.8830196,"domain_scores_codex":[0.9735834,0.01328954,0.0006076684,0.0008656413,0.005943573,0.005710133],"domain_scores_gemma":[0.9576622,0.01562868,0.002371988,0.0004205495,0.009697494,0.01421904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001656716,0.00007145637,0.01857678,0.0006849013,0.00002981811,0.00232359,0.8824065,0.0004453879,0.001636435,0.02429527,0.03740047,0.03196383],"study_design_scores_gemma":[0.00004749958,0.00009252231,0.01519292,0.001239763,0.0000488819,0.001111579,0.7465582,0.0004296096,0.0007173608,0.004544242,0.2298358,0.0001815563],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4324383,0.01279421,0.0040724,0.4654427,0.001721311,0.000172949,0.0002259696,0.00004712399,0.08308506],"genre_scores_gemma":[0.9622672,0.005483094,0.001784143,0.02562429,0.0001770707,0.00005284568,0.00005243993,0.0000239822,0.004534977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.117278,"threshold_uncertainty_score":0.6152139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032349645940213,"score_gpt":0.3157656490977108,"score_spread":0.2954421526383086,"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."}}