{"id":"W2958008382","doi":"10.1101/700179","title":"The CIViC knowledge model and standard operating procedures for curation and clinical interpretation of variants in cancer","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"National Cancer Institute; National Center for Advancing Translational Sciences; National Human Genome Research Institute; National Institutes of Health; Institute of Clinical and Translational Sciences","keywords":"Data curation; Interpretation (philosophy); Statement (logic); Data science; Computer science; Precision medicine; Computational biology; Knowledge management; Biology; Political science; 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":[0.03122133,0.002054736,0.001253809,0.008293461,0.001760143,0.009747328,0.004255177,0.003507486,0.01371697],"category_scores_gemma":[0.1033021,0.002095827,0.003487325,0.004125988,0.002545126,0.005820705,0.009053993,0.005072237,0.0121892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002910734,"about_ca_system_score_gemma":0.007336021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006228746,"about_ca_topic_score_gemma":0.004677627,"domain_scores_codex":[0.9708172,0.011005,0.004090055,0.004015351,0.00906148,0.00101093],"domain_scores_gemma":[0.9164212,0.03891465,0.004969326,0.02559322,0.01251188,0.001589682],"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":[0.001381289,0.0006146726,0.008371206,0.001297242,0.0004599151,0.001061053,0.001822906,0.0304229,0.008197248,0.2416554,0.1826071,0.5221091],"study_design_scores_gemma":[0.0004809362,0.000163247,0.002588527,0.001041027,0.0002206891,0.001168175,0.0005403937,0.3737368,0.03639998,0.3654733,0.2178346,0.0003523193],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001410011,0.0000980707,0.9591045,0.0006534466,0.00007045227,0.0006928903,0.002486099,0.03318522,0.002299323],"genre_scores_gemma":[0.02175104,0.0001477835,0.9629464,0.0003891847,0.00008557183,0.0009389765,0.007167664,0.004123592,0.002449846],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03122133,"threshold_uncertainty_score":0.1651161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594342946607142,"score_gpt":0.2971486355708163,"score_spread":0.2812052061047449,"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."}}