{"id":"W2559928987","doi":"10.1371/journal.pmed.1002189","title":"Sequencing Strategies to Guide Decision Making in Cancer Treatment","year":2016,"lang":"en","type":"article","venue":"PLoS Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency","funders":"","keywords":"Cancer; Genomic sequencing; Medicine; Perspective (graphical); MEDLINE; Cancer treatment; Computational biology; Data science; Bioinformatics; Biology; Computer science; Genome; Genetics; Internal medicine; Artificial intelligence","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":[0.04785001,0.001666408,0.001613002,0.002827084,0.002101422,0.006706455,0.003212975,0.006868697,0.008977755],"category_scores_gemma":[0.1024133,0.000759309,0.001567677,0.001953725,0.006335138,0.008662421,0.00401894,0.009974977,0.002347107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004046744,"about_ca_system_score_gemma":0.009236712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003855746,"about_ca_topic_score_gemma":0.007777249,"domain_scores_codex":[0.9805616,0.01507563,0.0009045532,0.0008286236,0.002086971,0.0005427355],"domain_scores_gemma":[0.9284189,0.06260714,0.00203623,0.00157695,0.003807753,0.001553082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003606089,0.0002136384,0.004260951,0.001543762,0.0004886968,0.0005686241,0.002356939,0.02219787,0.001098381,0.3687854,0.133754,0.4643712],"study_design_scores_gemma":[0.0000694823,0.0001463958,0.0007532195,0.00147823,0.0001465448,0.0003406569,0.001035851,0.007772192,0.0009343997,0.7838525,0.2033796,0.00009080731],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003379671,0.0860351,0.2448138,0.6303592,0.007413381,0.0002988718,0.0004433346,0.0003878903,0.02686876],"genre_scores_gemma":[0.1863681,0.1297947,0.4505899,0.2015063,0.01489152,0.001474749,0.0009104434,0.0005971518,0.01386724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04785001,"threshold_uncertainty_score":0.253058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405214067593124,"score_gpt":0.336237746966759,"score_spread":0.3021856062908277,"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."}}