{"id":"W2913691662","doi":"10.1016/j.pathol.2018.12.085","title":"Molecular Tumor Board – Interesting Cases From A Cancer Genomics Program","year":2019,"lang":"en","type":"article","venue":"Pathology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Genomics; Personalized medicine; Precision medicine; Medicine; Computational biology; Exome; Exome sequencing; Bioinformatics; Genome; Biology; Pathology; Gene; 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":[],"consensus_categories":[],"category_scores_codex":[0.00006608769,0.0001619801,0.0001902774,0.00003003292,0.00003166993,0.00002731613,0.0001925697,0.0001143926,0.0000782615],"category_scores_gemma":[0.0001285653,0.0001697639,0.00008691789,0.00004425939,0.00006251186,0.000001770377,0.0002201524,0.00009985156,0.00006102631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003425149,"about_ca_system_score_gemma":0.0001217797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003603128,"about_ca_topic_score_gemma":0.0003239084,"domain_scores_codex":[0.9989727,0.00004370647,0.0001871336,0.0004476646,0.0000542541,0.0002945549],"domain_scores_gemma":[0.9993407,0.00004048456,0.00009424295,0.0003891155,0.00005859848,0.00007687569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009748344,0.00007813598,0.03433557,0.00001506464,0.00005202066,0.0003051468,0.00006706188,0.0004276173,0.9487966,0.00009732399,0.0006499822,0.01507793],"study_design_scores_gemma":[0.002521537,0.002082986,0.02242989,0.00006991635,0.000149909,0.000424939,0.0002683177,0.001178508,0.6895306,0.0007588,0.2793994,0.001185165],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949958,0.002757038,0.0002651413,0.000119054,0.0005869996,0.000329279,0.0001513909,0.0000254959,0.0007697793],"genre_scores_gemma":[0.9948069,0.0002462981,0.002971279,0.00128935,0.0002985251,0.0001170483,0.0001496819,0.00003920919,0.00008168206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2787494,"threshold_uncertainty_score":0.6922769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01117405648617659,"score_gpt":0.2730461901008794,"score_spread":0.2618721336147028,"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."}}