{"id":"W2031320118","doi":"10.1016/s1470-2045(14)71113-1","title":"RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1","year":2014,"lang":"en","type":"article","venue":"The Lancet Oncology","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":270,"is_retracted":false,"has_abstract":false,"ca_institutions":"Genome British Columbia","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health; Prostate Cancer Foundation; Doris Duke Charitable Foundation; Howard Hughes Medical Institute; U.S. Department of Defense","keywords":"Prostate cancer; Oncology; Prostatectomy; Medicine; Cohort; Internal medicine; Tumor progression; Multivariate analysis; Prostate-specific antigen; Cancer","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00111201,0.0001650561,0.0005259321,0.0001845305,0.00008142843,0.00001101004,0.0003056763,0.0001256814,0.0000244416],"category_scores_gemma":[0.0002294791,0.0001054694,0.00009338214,0.0008827486,0.0003380429,0.000005075867,0.0001314163,0.0002128455,0.000001834649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001665453,"about_ca_system_score_gemma":0.0004477563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005575425,"about_ca_topic_score_gemma":0.00748793,"domain_scores_codex":[0.9981053,0.0005928742,0.00027767,0.0003651446,0.0002613625,0.0003976866],"domain_scores_gemma":[0.999082,0.00007219664,0.0002360117,0.0003904618,0.0001629546,0.00005643302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003818542,0.0008279513,0.005035709,0.00008824869,0.007728572,0.00004756561,0.0004636714,0.04582597,0.9019241,0.001444591,0.0008157401,0.03197929],"study_design_scores_gemma":[0.03791988,0.007279832,0.07904319,0.0005966653,0.004165956,0.00005320325,0.0004660964,0.1024581,0.7313567,0.0005964168,0.03463061,0.001433367],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814146,0.0008726171,0.01503534,0.001300313,0.0001134732,0.0006879263,0.0001312721,0.00001938917,0.0004250348],"genre_scores_gemma":[0.9959933,0.0003851911,0.002744189,0.0002407859,0.00005440346,0.0002083427,0.0002737803,0.00001835682,0.00008162051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1705674,"threshold_uncertainty_score":0.4300916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02743596481291197,"score_gpt":0.354650894791416,"score_spread":0.327214929978504,"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."}}