{"id":"W2586531649","doi":"10.1016/s0140-6736(17)30282-9","title":"Biopsy transcriptome expression profiling: proper validation is key","year":2017,"lang":"en","type":"letter","venue":"The Lancet","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cross-validation; Logistic regression; Overfitting; Gene expression profiling; Transcriptome; Medicine; Microarray; Computer science; Gene signature; Computational biology; Bioinformatics; Artificial intelligence; Gene; Internal medicine; Gene expression; Biology; Genetics","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":[0.01149359,0.0006864881,0.002232862,0.0007636117,0.001736738,0.004869693,0.001961475,0.02323347,0.005334879],"category_scores_gemma":[0.06429135,0.0005952124,0.001112638,0.000688405,0.003952095,0.004941362,0.002091762,0.04156728,0.007395418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002777272,"about_ca_system_score_gemma":0.004376068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001965795,"about_ca_topic_score_gemma":0.004358018,"domain_scores_codex":[0.9915229,0.003538807,0.001267765,0.0007512546,0.002346894,0.0005724209],"domain_scores_gemma":[0.930647,0.04746323,0.00266262,0.002244975,0.01272254,0.004259682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008799405,0.00003922212,0.001417183,0.0001826873,0.00003343711,0.0004918245,0.0000773811,0.00008817776,0.0003296366,0.003441891,0.9486982,0.04511245],"study_design_scores_gemma":[0.0002335051,0.0001129128,0.002311943,0.001220411,0.00008671883,0.001609884,0.0007018667,0.0007339045,0.00079673,0.05545357,0.9366122,0.0001262666],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001682153,0.003425884,0.0003485284,0.9816446,0.01352183,0.000005267174,0.0000828347,0.00002977843,0.0007730766],"genre_scores_gemma":[0.004280879,0.005121844,0.00106469,0.9125226,0.07490946,0.00004374223,0.00011816,0.00004136079,0.00189731],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02323347,"threshold_uncertainty_score":0.06078464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08540005573221829,"score_gpt":0.3367507157089875,"score_spread":0.2513506599767692,"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."}}