{"id":"W2980154814","doi":"10.1182/blood.v112.11.735.735","title":"An Automated and Quantitative Protein Expression-Based Classification System to Identify High Risk Multiple Myeloma Patients","year":2008,"lang":"en","type":"article","venue":"Blood","topic":"Multiple Myeloma Research and Treatments","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multiple myeloma; Medicine; Oncology; Autologous stem-cell transplantation; Internal medicine; Lenalidomide; Gene expression profiling; Transplantation; Tissue microarray; Immunohistochemistry; Bioinformatics; Cancer research; Gene expression; Biology","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":[0.001097949,0.0006142861,0.0006758651,0.001866627,0.0003283654,0.0008857361,0.0005688455,0.0005507103,0.002081294],"category_scores_gemma":[0.002112715,0.0001709244,0.0004097532,0.0006421045,0.0001910045,0.0003543929,0.0004486775,0.0004047719,0.001013111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007698221,"about_ca_system_score_gemma":0.0006883955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002465152,"about_ca_topic_score_gemma":0.002106036,"domain_scores_codex":[0.9992839,0.000148654,0.00007766918,0.0002274477,0.00019402,0.00006831303],"domain_scores_gemma":[0.9984628,0.0004859384,0.0002519017,0.00009831665,0.0006039012,0.0000971831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002644612,0.001205715,0.3736508,0.0002787804,0.0003509917,0.0003569711,0.000183494,0.04785724,0.1067945,0.0008081306,0.01128119,0.4545875],"study_design_scores_gemma":[0.000125058,0.0005871358,0.1798628,0.00003392854,0.0001590891,0.0005137736,0.0001096586,0.7841429,0.03043325,0.0009749015,0.002995677,0.00006186141],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7077784,0.0004192498,0.2749819,0.0005678593,0.0001026295,0.000598265,0.005691613,0.00598545,0.003874663],"genre_scores_gemma":[0.8756428,0.00008182438,0.1180193,0.0001304368,0.00005624105,0.0003796482,0.004330519,0.00005431888,0.001304773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002465152,"threshold_uncertainty_score":0.006962657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03410212296008718,"score_gpt":0.3328350364231912,"score_spread":0.298732913463104,"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."}}