{"id":"W2741424057","doi":"10.1158/1538-7445.am2017-1286","title":"Abstract 1286: Targeted deep sequencing of colorectal tumor tissues to study associations of tumor subtypes with germline genetic, lifestyle, and environmental risk factors","year":2017,"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":"Ontario Institute for Cancer Research","funders":"","keywords":"KRAS; Colorectal cancer; Biology; Microsatellite instability; DNA sequencing; Genetics; Whole genome sequencing; Germline; Penetrance; Germline mutation; Exome sequencing; Cancer; Genome; Microsatellite; Gene; Mutation","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":[0.0008111777,0.0005774639,0.000711229,0.0008969551,0.000405653,0.0007595436,0.000460262,0.0005852004,0.00413788],"category_scores_gemma":[0.001598238,0.0004965618,0.0007202842,0.0008231202,0.0002222165,0.0001975337,0.0007250914,0.0007673348,0.001672713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003960846,"about_ca_system_score_gemma":0.0005556484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002732489,"about_ca_topic_score_gemma":0.007011516,"domain_scores_codex":[0.9992962,0.0001323255,0.00004398498,0.0002759574,0.0001738058,0.00007780773],"domain_scores_gemma":[0.9994251,0.0001604185,0.0001044488,0.00008930772,0.0001424411,0.00007824478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002197929,0.0002930304,0.09383433,0.001284956,0.001227078,0.0007616521,0.0005684991,0.005732736,0.7606204,0.001786969,0.02165339,0.1100391],"study_design_scores_gemma":[0.0008389407,0.002242737,0.5522107,0.000311511,0.001196631,0.004976237,0.0005616172,0.06569068,0.2353598,0.005448852,0.1309855,0.0001767997],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7488304,0.003359305,0.1263789,0.0007758606,0.0002363876,0.0008520922,0.108139,0.002113487,0.00931451],"genre_scores_gemma":[0.7341065,0.001332168,0.1470785,0.001430354,0.0001277542,0.001754147,0.09851045,0.001067163,0.01459302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00413788,"threshold_uncertainty_score":0.01384258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02759906566476314,"score_gpt":0.3325136015154231,"score_spread":0.30491453585066,"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."}}