{"id":"W2004738774","doi":"10.2217/pme.09.43","title":"Use of Personalized Medicine in the Selection of Patients for Renal Transplantation: Views of Quebec Transplant Physicians and Referring Nephrologists","year":2009,"lang":"en","type":"article","venue":"Personalized Medicine","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Hôpital Notre-Dame; Université de Montréal","funders":"","keywords":"Medicine; Transplantation; Renal transplant; Personalized medicine; Selection (genetic algorithm); Internal medicine; Family medicine; Intensive care medicine; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003871132,0.0002253608,0.0009034799,0.0002326488,0.00004330803,0.00000215176,0.0000779071,0.0000874612,0.00005697136],"category_scores_gemma":[0.00006722697,0.0001328541,0.0001361884,0.0003459154,0.0004223404,0.00009983977,0.000001602362,0.0001265202,1.2818e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003548476,"about_ca_system_score_gemma":0.00004754543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001554941,"about_ca_topic_score_gemma":0.0006780066,"domain_scores_codex":[0.9981004,0.0001245445,0.0007900545,0.0002555495,0.0005280253,0.0002014149],"domain_scores_gemma":[0.9987529,0.0004750291,0.000361347,0.0001410168,0.0002073467,0.00006236578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.06260715,0.003725113,0.4141915,0.01168888,0.001639002,0.0001070635,0.1692903,0.00008761983,0.2709746,0.01179838,0.001287439,0.05260292],"study_design_scores_gemma":[0.03430559,0.00510699,0.945502,0.005076255,0.001913243,0.00008455446,0.0009439272,0.0003003774,0.004638326,0.0001491093,0.001800794,0.0001788113],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920662,0.00115157,0.002147507,0.002821641,0.0000555822,0.001301878,0.0001368219,0.00001425105,0.000304556],"genre_scores_gemma":[0.9948544,0.002271321,0.001045954,0.00106268,0.00007022676,0.00003261145,0.0004587423,0.00001312382,0.0001909228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5313105,"threshold_uncertainty_score":0.5417632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06504543306035908,"score_gpt":0.3329588573331126,"score_spread":0.2679134242727535,"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."}}