{"id":"W2801588305","doi":"10.1200/po.17.00198","title":"Comparative RNA-Sequencing Analysis Benefits a Pediatric Patient With Relapsed Cancer","year":2018,"lang":"en","type":"article","venue":"JCO Precision Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alex's Lemonade Stand Foundation for Childhood Cancer; Canadian Institutes of Health Research; National Cancer Institute; BC Cancer Foundation; St. Baldrick's Foundation; Canada Research Chairs","keywords":"Pediatric cancer; Gene; RNA; DNA sequencing; Computational biology; Biology; Cancer; Genome; Cancer genome sequencing; Genetics; Medicine; Genomics; Bioinformatics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000141514,0.0001769975,0.0003567734,0.0001556155,0.0001315594,0.00002251176,0.0001799153,0.0001988709,0.0001353831],"category_scores_gemma":[0.00007899111,0.0001451898,0.0001073244,0.0004710406,0.0001047548,0.000004134309,0.0001541371,0.0001069216,0.00002408891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000181194,"about_ca_system_score_gemma":0.0005092547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001766627,"about_ca_topic_score_gemma":0.003711525,"domain_scores_codex":[0.9986858,0.00007736158,0.0002996963,0.0004955229,0.000163164,0.0002783894],"domain_scores_gemma":[0.9987935,0.0001032112,0.000230777,0.0003712876,0.0003700292,0.000131137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01048977,0.001703442,0.1202267,0.0000599928,0.005453662,0.0001803558,0.009748558,0.08200581,0.2391389,0.001183637,0.09686197,0.4329472],"study_design_scores_gemma":[0.007400237,0.02973272,0.07556336,0.00006397095,0.003735829,0.0001018315,0.001303427,0.00447456,0.3248604,0.0003402844,0.5503129,0.002110556],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913609,0.0016006,0.001845159,0.0001372878,0.0003613142,0.0002420881,0.00006066942,0.00001159524,0.004380367],"genre_scores_gemma":[0.9946836,0.001160575,0.002547329,0.0006136842,0.0006428323,0.000081796,0.00006749809,0.00001688863,0.0001858478],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4534509,"threshold_uncertainty_score":0.592067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02454373684747535,"score_gpt":0.3100861393035607,"score_spread":0.2855424024560853,"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."}}