{"id":"W3087252472","doi":"10.3390/genes11091098","title":"Building towards Precision Oncology for Pancreatic Cancer: Real-World Challenges and Opportunities","year":2020,"lang":"en","type":"review","venue":"Genes","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Precision oncology; Profiling (computer programming); Pancreatic cancer; Precision medicine; Pancreatic ductal adenocarcinoma; Clinical Oncology; Biomarker discovery; Medicine; Germline; DNA sequencing; Biomarker; Computational biology; Bioinformatics; Oncology; Internal medicine; Cancer; Biology; Gene; Computer science; Proteomics; Pathology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006183489,0.0003976185,0.003704849,0.0003111693,0.0001153263,0.00001644593,0.0001766166,0.0004146221,0.00009407764],"category_scores_gemma":[0.0002101354,0.0002891097,0.0002897422,0.0001113382,0.0001847601,0.00003732436,0.0001745601,0.0003173611,0.000008560918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003689769,"about_ca_system_score_gemma":0.002371775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000160381,"about_ca_topic_score_gemma":0.0001542594,"domain_scores_codex":[0.9978836,0.0002408552,0.0006213463,0.00056333,0.0002536467,0.0004372262],"domain_scores_gemma":[0.9980622,0.0009090013,0.0002750955,0.0003161686,0.0001420735,0.0002954478],"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":[0.00009141764,0.00003851688,0.00002488318,0.03087948,0.000608512,0.0001067353,0.0001730369,1.957698e-8,0.000001401305,0.001225878,0.001304498,0.9655456],"study_design_scores_gemma":[0.0005901159,0.0008628131,0.000084504,0.009071825,0.004196627,0.0001307078,0.0001260262,0.00004725422,0.000001978762,0.0002722836,0.9843832,0.0002326921],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007001821,0.987202,0.00006001908,0.00257263,0.0002208542,0.002143749,0.00008372806,0.00006568264,0.007581313],"genre_scores_gemma":[0.000003793306,0.9878607,0.005568918,0.0001226006,0.001019668,0.00170848,0.00007147613,0.00007320555,0.003571136],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9830787,"threshold_uncertainty_score":0.9999561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3745743232346988,"score_gpt":0.4934109164994511,"score_spread":0.1188365932647523,"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."}}