{"id":"W2276803021","doi":"10.1101/mcs.a000570","title":"Lessons learned from the application of whole-genome analysis to the treatment of patients with advanced cancers","year":2015,"lang":"en","type":"article","venue":"Molecular Case Studies","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre; BC Children's Hospital; BC Cancer Agency","funders":"BC Cancer Foundation; University of British Columbia; Canada Research Chairs","keywords":"Multidisciplinary approach; Genome; Precision medicine; Medicine; Cancer; Genomics; Computational biology; DNA sequencing; Personalized medicine; Biomarker; Bioinformatics; Oncology; Gene; Internal medicine; Biology; Genetics; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02352013,0.0009212173,0.001106721,0.00120783,0.0006879816,0.004345251,0.00236909,0.002930239,0.001526984],"category_scores_gemma":[0.04245939,0.0004319097,0.001332522,0.001022644,0.003795204,0.004327311,0.002096013,0.008613222,0.000559008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001970184,"about_ca_system_score_gemma":0.003285494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007163138,"about_ca_topic_score_gemma":0.007907497,"domain_scores_codex":[0.9932568,0.004496487,0.0003850385,0.0006670636,0.0009737105,0.000220765],"domain_scores_gemma":[0.9712455,0.0214961,0.0006683565,0.001990931,0.003040405,0.001558571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"case_report","study_design_scores_codex":[0.0005482491,0.0003372923,0.03856871,0.00210098,0.000625799,0.002131028,0.00242567,0.01336433,0.002813458,0.03479905,0.09819194,0.8040935],"study_design_scores_gemma":[0.0004884284,0.001342768,0.06048271,0.005925202,0.0006543766,0.005310053,0.007584993,0.03403125,0.006148557,0.4801177,0.3974478,0.0004661577],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04516898,0.1382798,0.07190686,0.7240207,0.004844591,0.0001781086,0.0006041742,0.0006658023,0.01433097],"genre_scores_gemma":[0.4565584,0.2175277,0.1812633,0.126089,0.01396191,0.000313151,0.0007171619,0.0005829696,0.002986625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02352013,"threshold_uncertainty_score":0.1243878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555076050726099,"score_gpt":0.2989569403939088,"score_spread":0.2734061798866478,"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."}}