{"id":"W2561110796","doi":"10.1158/1538-7445.am2015-4743","title":"Abstract 4743: A population-based approach to address clinical cancer care: The national genomics platform","year":2015,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Genomics; Big data; Informatics; Data science; Computer science; Scalability; Precision medicine; Personalized medicine; Medicine; Bioinformatics; Genome; Data mining; Biology; Engineering; Database; Genetics","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":[0.01753348,0.0005083014,0.0006473116,0.001706767,0.001055381,0.003044439,0.002719604,0.002131789,0.02577933],"category_scores_gemma":[0.02071831,0.0003565625,0.0008787246,0.002779935,0.0005454406,0.002335349,0.006245649,0.002946996,0.005501126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003231577,"about_ca_system_score_gemma":0.01346594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01961351,"about_ca_topic_score_gemma":0.01472207,"domain_scores_codex":[0.9937571,0.003858358,0.0002506438,0.0008179318,0.0009156979,0.0004001672],"domain_scores_gemma":[0.9897371,0.002675489,0.0005800222,0.001190543,0.002875976,0.002940753],"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":[0.0003232598,0.0007115205,0.05435924,0.0003205549,0.0002528001,0.0003843655,0.000836591,0.006631604,0.001086518,0.03370261,0.430304,0.471087],"study_design_scores_gemma":[0.0009577441,0.0009858048,0.1282326,0.001388135,0.0003937571,0.0006315956,0.00255461,0.05451306,0.001776048,0.1530567,0.6552613,0.0002485416],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07693463,0.004917044,0.2267534,0.3824564,0.00579099,0.005782926,0.0478056,0.008762145,0.2407969],"genre_scores_gemma":[0.3984109,0.005767364,0.423671,0.07969724,0.004044065,0.01198708,0.03658116,0.001615687,0.03822548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02577933,"threshold_uncertainty_score":0.09272701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2202195237759935,"score_gpt":0.464918255005626,"score_spread":0.2446987312296325,"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."}}